Linked Open Data for Digital Humanities 9781000929799, 9781032055152, 9781032055183, 9781003197898

Table of contents :
Cover
Half Title
Series
Title
Copyright
Dedication
Contents
Preface
Context for This Book
How to Read This Book
Who Should Read This Book
Structure of the Book
Conventions Used in This Book
In Summary
Acknowledgements
Abbreviations
Bibliography
1 A False Dichotomy
1.1 Preamble
1.2 Interdisciplinarity
1.3 Snow’s Two Cultures
1.4 The Role of Humanities in Computer Science
1.5 Linked Data and the Humanities – A Perfect Pair?
1.5.1 The First Wittgenstein and the RDF Triple
1.5.2 The Second Wittgenstein and Explicit Statement of Facts
1.5.3 Foucault, Semantic Web, and Properties
1.5.4 Meaning Derived From Connections
1.6 Conclusion
Bibliography
2 Privacy, Ethics, and Trust
2.1 Preamble
2.2 The Unavoidable Orwellian Reference
2.3 Privacy as a Right, Not a Privilege
2.4 Privacy Paradox
2.5 Trusting Data Producers, Trusting Data Consumers
2.6 Potential for Disaster
2.7 We Need to Talk About Strava
2.8 On the Questions of Ethics
2.9 Conclusion
Bibliography
3 Closed But Not for Business
3.1 Preamble
3.2 Data
3.2.1 Unreproducible Data
3.2.2 Unstructured Data
3.2.3 Ambiguous Data
3.2.4 Incomplete Data
3.2.5 Messy Data
3.4 Openness
3.4.1 Open Source
3.4.2 Open Access
3.4.3 Open Data
3.4.4 Linked Open Data
3.5 Be FAIR, and CARE
3.6 To Be or Not to Be Open?
3.7 Solutions Combining Accessible and Inaccessible Data
3.8 Case Study: The ElePHãT Project (Bibliographic Metadata)
3.9 Conclusion
Bibliography
4 “Truth” and Bias
4.1 Preamble
4.2 Ontologies
4.3 Bias in Ontologies
4.4 Document Your Design
4.5 Justify Your Choices
4.6 Case Study: Old Babylonian Literature
4.6.1 Choosing the Case Study
4.6.2 Three Ox-Drivers of Adab
4.6.3 Ontological Representation
4.6.3.1 Material Objects in Museums
4.6.3.2 Bibliographical Metadata
4.6.3.3 Narrative Structure
4.7 Conclusion
Bibliography
5 Data Demands
5.1 Preamble
5.2 The More Things Change
5.3 Bias in Tool Recommendations
5.4 Different Demands of Different Technologies
5.6 Case Study: JazzCats
5.6.1 Tabular Data: The Body and Soul Discography
5.6.2 Relational Databases to RDF: Weimar Jazz Database
5.6.3 Ready-Made RDF: Linked Jazz
5.6.4 Querying Across Three Datasets Using SPARQL
5.7 Conclusion
Bibliography
6 Future Directions
6.1 Preamble
6.2 The Non-Linear Approach to Discussing Linked Data in the Digital Humanities
6.3 The Tech-Focused Summary of Linked Data in the Digital Humanities
6.4 Conclusion
Bibliography
Glossary
Index

Citation preview

Linked Data for Digital Humanities

Linked Open Data for Digital Humanities provides insights into how digital technologies can enrich and diversify humanities scholarship and make it pioneering in the digital age. Written in non-specialist language, the book illustrates how information is captured, published, represented, accessed, and interpreted using computational systems and, in doing so, shows how technologies actively shape the way we understand what we encounter. Focusing as it does on underlying Web architecture and projects accessible online, the book has an inherently international focus. The interdisciplinary case study examples include bibliographic data from works published in England between 1470 and 1700; literature from ancient Iraq; jazz performances, predominantly from the USA in the 1930s; and even reach as far as an alien, fictional future. Whilst these case study examples span vast spatiotemporal distances, they all share a common thread in the use of the Linked Data information publication paradigm. Using existing computer science methods, as well as processes such as ontology development and database design, the book also includes reflections on practical considerations and offers advice about how to take institutional policies, socio-cultural sensitivities, and economic models into consideration when implementing Linked Data projects. Linked Open Data for Digital Humanities discusses technological issues in the context of humanities scholarship, bridging disciplines and enabling informed conversations across disciplinary boundaries. It will be of interest to humanities scholars, computer and data scientists, and library and information scientists. Dr Terhi Nurmikko-Fuller is Senior Research Fellow at the Centre for Social Research & Methods at the Australian National University. Her research focuses on interdisciplinary experimentation into ways digital technologies and computational methods can be used to support and diversify research in the humanities, arts, and social sciences in general, and in relation to public culture, including Web Science, and the cultural heritage sector in particular. Terhi’s publications centre on topics related to Linked Data but cover a range of others from the role of gamification and informal online environments in education to 3D digital models of items in museums in the UK and Australia.

Digital Research in the Arts and Humanities Founding Series Editors: Marilyn Deegan, Lorna Hughes and Harold Short Current Series Editors: Lorna Hughes, Nirmala Menon, Andrew Prescott, Isabel Galina Russell, Harold Short and Ray Siemens

Digital technologies are increasingly important to arts and humanities research, expanding the horizons of research methods in all aspects of data capture, investigation, analysis, modelling, presentation and dissemination. This important series covers a wide range of disciplines with each volume focusing on a particular area, identifying the ways in which technology impacts on specific subjects. The aim is to provide an authoritative reflection of the ‘state of the art’ in technologyenhanced research methods. The series is critical reading for those already engaged in the digital humanities, and of wider interest to all arts and humanities scholars. The following list includes only the most-recent titles to publish within the series. A list of the full catalogue of titles is available at: www.routledge.com/ Digital-Research-in-the-Arts-and-Humanities/book-series/DRAH Transformative Digital Humanities Challenges and Opportunities Edited by Mary McAleer Balkun and Marta Mestrovic Deyrup Medieval Manuscripts in the Digital Age Edited by Benjamin Albritton, Georgia Henley and Elaine Treharne Access and Control in Digital Humanities Edited by Shane Hawkins Information and Knowledge Organisation in Digital Humanities Global Perspectives Edited by Koraljka Golub and Ying-Hsang Liu Networks and the Spread of Ideas in the Past: Strong Ties, Innovation and Knowledge Exchange Edited by Anna Collar

For more information about this series, please visit: https://www.routledge.com/Digital-Researchin-the-Arts-and-Humanities/book-series/DRAH

Linked Data for Digital Humanities Terhi Nurmikko-Fuller

First published 2024 by Routledge 4 Park Square, Milton Park, Abingdon, Oxon OX14 4RN and by Routledge 605 Third Avenue, New York, NY 10158 Routledge is an imprint of the Taylor & Francis Group, an informa business © 2024 Terhi Nurmikko-Fuller The right of Terhi Nurmikko-Fuller to be identified as author of this work has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-1-032-05515-2 (hbk) ISBN: 978-1-032-05518-3 (pbk) ISBN: 978-1-003-19789-8 (ebk) DOI: 10.4324/9781003197898 Typeset in Times New Roman by Apex CoVantage, LLC

Tytöille – for the girls

Contents

Preface Context for This Book How to Read This Book Who Should Read This Book Structure of the Book Conventions Used in This Book In Summary Acknowledgements Abbreviations Bibliography 1

A False Dichotomy

x xi xiii xv xvi xvii xviii xx xxii xxv 1

1.1 1.2 1.3 1.4 1.5

Preamble 1 Interdisciplinarity 2 Snow’s Two Cultures 5 The Role of Humanities in Computer Science 10 Linked Data and the Humanities – A Perfect Pair? 12 1.5.1 The First Wittgenstein and the RDF Triple 13 1.5.2 The Second Wittgenstein and Explicit Statement of Facts 14 1.5.3 Foucault, Semantic Web, and Properties 16 1.5.4 Meaning Derived From Connections 17 1.6 Conclusion 18 Bibliography 21 2

Privacy, Ethics, and Trust 2.1 2.2 2.3 2.4 2.5

Preamble 23 The Unavoidable Orwellian Reference 24 Privacy as a Right, Not a Privilege 26 Privacy Paradox 28 Trusting Data Producers, Trusting Data Consumers 30

23

viii Contents 2.6 Potential for Disaster 32 2.7 We Need to Talk About Strava 33 2.8 On the Questions of Ethics 36 2.9 Conclusion 37 Bibliography 39 3

Closed But Not for Business

42

3.1 Preamble 42 3.2 Data 43 3.2.1 Unreproducible Data 45 3.2.2 Unstructured Data 46 3.2.3 Ambiguous Data 47 3.2.4 Incomplete Data 48 3.2.5 Messy Data 49 3.4 Openness 50 3.4.1 Open Source 51 3.4.2 Open Access 52 3.4.3 Open Data 53 3.4.4 Linked Open Data 54 3.5 Be FAIR, and CARE 56 3.6 To Be or Not to Be Open? 56 3.7 Solutions Combining Accessible and Inaccessible Data 58 3.8 Case Study: The ElePHãT Project (Bibliographic Metadata) 59 3.9 Conclusion 62 Bibliography 64 4

“Truth” and Bias 4.1 4.2 4.3 4.4 4.5 4.6

Preamble 66 Ontologies 67 Bias in Ontologies 69 Document Your Design 71 Justify Your Choices 73 Case Study: Old Babylonian Literature 75 4.6.1 Choosing the Case Study 76 4.6.2 Three Ox-Drivers of Adab 76 4.6.3 Ontological Representation 77 4.6.3.1 Material Objects in Museums 77 4.6.3.2 Bibliographical Metadata 79 4.6.3.3 Narrative Structure 79 4.7 Conclusion 81 Bibliography 84

66

Contents 5

Data Demands

ix 86

5.1 5.2 5.3 5.4 5.6

Preamble 86 The More Things Change 88 Bias in Tool Recommendations 90 Different Demands of Different Technologies 93 Case Study: JazzCats 94 5.6.1 Tabular Data: The Body and Soul Discography 95 5.6.2 Relational Databases to RDF: Weimar Jazz Database 96 5.6.3 Ready-Made RDF: Linked Jazz 97 5.6.4 Querying Across Three Datasets Using SPARQL 97 5.7 Conclusion 98 Bibliography 101 6

Future Directions

103

6.1 Preamble 103 6.2 The Non-Linear Approach to Discussing Linked Data in the Digital Humanities 104 6.3 The Tech-Focused Summary of Linked Data in the Digital Humanities 109 6.4 Conclusion 115 Bibliography 118 Glossary Index

119 122

Preface

This book discusses the Linked Data method in a critical theoretical framework, examined through a Digital Humanities lens. As described in Chapter 1, Linked Data is a way of doing things; of making information available online using existing Web technologies and architecture. It is an information publication paradigm. The fundamental concept behind the method is that by making information available online in ways that are accessible to both humans and software agents, it will be possible to begin aggregating all the data that has been published online, or at least all the data that has been published online using this method. In order to do so, we need to publish information online as structured data – these datasets are then interlinked with each other, creating an interconnected network of information. Software agents can navigate this decentralised network (or “knowledge graph”) across disciplinary silos and datasets. The great aim of this method is that eventually, it will be possible to benefit from collective and global knowledge: not just to visit webpages but for browsers to provide users with results to searches that give us answers, not just hyperlinks to sites with relevant content. The whole of the Web could become a database with truly global reach.

Context for This Book

Technological development, implementation, and uptake do not occur in isolation. Institutional, social, political, and financial considerations all play a role. By illustrating the ways information is captured, published, represented, accessed, and interpreted using computational systems, this book shows how technologies actively shape the way we understand what we encounter online. We no longer live in a world where digital and computational technologies are built exclusively to represent analogue systems, but instead find ourselves in a situation where these systems can have an effect on determining what information is accessed and how. Trawling through a library’s card catalogues has been replaced by the goal of optimising our keyword searches on Google so that the correct link is displayed at the top – or at least “above the fold”.1 Never has there been a greater need for digital literacy and critical evaluation of online content, or of the various processes utilised for knowledge representation and information retrieval. Thirty years ago, Sir Tim Berners-Lee (the inventor of the HyperText Transfer Protocol, and thus accredited with being the inventor of the Web) had a vision of a global hypermedia system that could meaningfully capture the entirety of human knowledge across the world. The Web as we know it today has yet to (fully) reach this goal – the vast majority of the information we encounter online is available for human eyes as text, image, and multimedia, but behind the scenes, data structures, which can fail to grasp the necessary nuance of the information they hold, dictate the way we discover this content. Knowledge representation using graph structures is a powerful computational method. The largest and dominant companies on the Web (Google and Facebook, for example) have been implementing graph technologies for years, but little knowledge of this has trickled down to the public, or the academic community, that stands to gain from it. The terms “Linked Data” and “Semantic Web” have yet to be fully absorbed into the public vernacular to the same extent as other online phenomena and technologies, such as social media and algorithms; regular users do benefit from these technologies but are rarely informed about them. This book addresses this knowledge gap in part and engages the reader in a journey of critical evaluation and practical implementation of one of the most powerful information publication paradigms the world has ever seen: Linked Data. The aim

xii

Context for This Book

is to illustrate and explain the affordances and the challenges of this technology, particularly as it applies to the Digital Humanities. Note 1 “Above the fold” is a term with its origins in traditional (paper-based) media. The upper half of the front page of a newspaper still remains the location for the most important news story or eye-catching headline. In web development, the term is used to describe that initial top of a webpage, specifically content that is visible to the user without scrolling down the page or clicking on something.

How to Read This Book

This book is a summary of heuristics from existing projects, navigating the space between theoretical possibilities and pragmatic implementation. In a very literal sense, it is a critical companion: “critical” not because it is exclusively negative, but because it incorporates analyses and discussions of the strengths and weaknesses of Linked Data. And, “companion”, because the aim of this book is to be read alongside technical manuals and handbooks, not instead of them. The aim is to show examples of existing projects, and explain how each has been affected by various different external considerations. As such, this book could potentially benefit anyone embarking on using Linked Data. There are many ways to amalgamate information (online or otherwise), and the Linked Data method is simply one of them. Similarly, there are many equally valid but different methods for creating Linked Data, depending on the project’s aims, the team’s skills, and the availability of resources such as servers. This book does not propose to be a definite guide to the method of Linked Data (tools, activities, etc.) or offer a set of step-by-step instructions to be followed to implement a project. Rather, its purpose is to engage the reader in an informed conversation about methodology of Linked Data: the important things that surround this method, its guiding principles, the theory behind it, and our core values as practitioners. Other publications have already fulfilled the function of a technical manual for Linked Data methods specifically, ranging from the early works of Allemang and Hendler’s Semantic Web for the Working Ontologist (2011) and DuCharme’s 2013 publication Learning SPARQL; there is also Hyvönen’s handbook for Linked Data (2018), and a plethora of newer ones (often with specific foci), including Theocharis and Tsihrintzis’ 2023 volume Production and Publication of Linked Open Data: The Case of Open Ontologies; indeed there is a more recent publication from Hyvönen (2022), too. For galleries, libraries, archives, and museums (GLAM) sector data in particular, there are a number of projects and prototypes, but in terms of technical how-to guides, it is worth giving Seth van Hooland and Ruben Verborgh’s (2014) Linked Data for Libraries, Archives and Museums a go. These books offer succinct summaries of terminologies, detailed explanations about processes, and offer practical advice. This book seeks to mirror and complement all these publications and approaches by including extra-technical considerations (institutional

xiv

How to Read This Book

policies, data access models, etc.) that affect the Linked Data paradigm in the conversation. The chapters and case studies tackle separate issues and different technical aspects of the Linked Data methodology. Although the chapters benefit from complementary information and examples, it is possible to opt not to read the book as a single narrative from beginning to end, but instead to dip into the book, reading one chapter at a time, to dive in deep with one particular aspect. The technical aspects that build up to an increasingly comprehensive picture give the book an internal cohesion that mirrors the Linked Data workshop at the Digital Humanities Oxford Summer School (Nurmikko-Fuller, 2022): first, the W3C standard Resource Description Framework (RDF) is introduced; this is followed by a description of ontologies and Web Ontology Language (OWL). The workflow and thus the chapters in this book culminate in a description of SPARQL Protocol and RDF Query Language (SPARQL).

Who Should Read This Book

An audience of academics and researchers, either involved in or interested in pursuing Linked Data as a tool – the “Linked Data curious” – was envisioned for this book. It is intended to fuel discussions of issues and topics that affect Digital Humanities researchers (referred to henceforth as “DHers”) in general; of those that involve Linked Data in particular; of those that we should all be aware of as citizens of a Digital Data Economy. Computer scientists can gain an understanding of why a successful Linked Data project requires more than a shift from one programming language to another (from JSON to JSON-LD); Humanities researchers can see ways in which Linked Data can support and enrich their existing research paradigms. This book has use for those who have yet to complete a Linked Data project and for those who want to expand their understanding of this technology and its affordances and limitations beyond just what is technologically necessary and possible. For interdisciplinary researchers in Digital Humanities, the book provides important insights into how to take institutional policies, socio-cultural sensitivities and economic models into consideration when implementing their Linked Data projects. It will be both of use and of interest to students across a vast range of disciplines, in and out of the broad church of Digital Humanities: DHers should engage with the book as a discipline-driven discussion and collection of case study examples of successful collaborations reaching over various different disciplinary borders. Written in non-specialist language, the book is intended to be of interest to data scientists, information professionals, library and information scientists, museums and cultural-heritage professionals, and historians of a vast range of specialisations looking for case study examples of the use of computational tools in their domains. It provides insights into how digital technologies can enrich and diversify traditional Humanities scholarship, and make it relevant and pioneering in the digital age. The aim was to keep the book sufficiently light, and the expression of the ideas similarly sufficiently clear to not deter any general reader who might be curious to find out more or have a budding interest in Linked Data, or even more generally just the topics discussed throughout the book.

Structure of the Book

The narrative begins by establishing the broader field of Digital Humanities as the context in which the rest of the book is to be understood. Core concepts and jargon are sprinkled throughout the book, acronyms defined where relevant and the nomenclature of the technology and the methodology deconstructed with the introduction of the idea they refer to. In doing so, it is possible to establish a shared vocabulary without overloading the text with jargon or details of technologies; to minimise miscommunication and discussions that are at cross-purpose. This is particularly important since the community of practice that has grown around this methodology has borrowed from others very generously: ontology, record, resource, subject, object, and so on. The list of words that mean different things is long and needs to be explicitly articulated. Beyond the technological aspects, each chapter, in turn, examines a different issue, such as institutional policies, economic models of higher education establishments and memory institutions, and the inherent biases of information structures such as databases. Case study examples in Chapters 3, 4, and 5 illustrate different practical and pragmatic considerations that every Linked Data practitioner in the Humanities context (either at centre stage or on the periphery) should be aware of before commencing a project. The book finishes with a brief indulgence in future gazing: given all that we know now, where do we perceive Linked Data will take us next?

Conventions Used in This Book

The following typographical conventions are used in this book: • Italics – for emphasis (as well as for titles and names of statues). • Courier New – to distinguish specific expressions of RDF, SPARQL queries, and ontology-related terminology from the main body of text. Sample data snippets, code, and prefixes are similarly in this font. • Punctuation is outside of quote marks, unless incorporated into a string of characters, for example, as the instance for the Object in an RDF triple. • Most of the HTTP URIs mentioned in this book will not point to a page, video, image, audio file, or website. They represent abstract notions and the relationships between things. Attempts to click on these (e.g. should they appear as hyperlinks in a PDF) will systematically and inevitably return a 404 error code. This is because these identifiers are uniform resource identifiers (URIs) and not uniform resource locators (URLs): they are not pointing to a specific location where a resource exists; there is no digital file, page, or other resource for the browser to display. Where possible, Example.org is used in URIs to emphasise the fact that they refer to illustrative examples. In some cases, the URIs are genuine examples of data from various projects and the base URI will reflect that. They will return a 404, for the reasons outlined earlier.

In Summary

In this book, the Linked Data method and its processes will be described in the context of the Digital Humanities. The aim is to demystify key concepts to allow greater access and engagement with the technology. The aim is to enable an ever-increasing number of practitioners to benefit from the computational power of Linked Data and graph databases. The book provides context and a critical framework for the thoughtful development of Linked Data projects. All of this is achieved through heuristics accumulated in various projects, each focusing on a different area of investigation. Comprehensive engagement with the topic (Linked Data), particularly in its interdisciplinary context (Digital Humanities), requires a robust understanding of both: we can only truly see the value of the method for the latter with a sufficient understanding of the opportunities and limitations of the former. We will need to explore the messy, complicated, incomplete, and ambiguous data of the Humanities, and evaluate where (and how) Linked Data can (and cannot) be used to support scholarship and investigation. And, in turn, where these challenges of the data can be used to test the robustness of this technology itself. There is a lot to unravel, and each of the topics is, in and of itself, worthy of a multitude of volumes, not just a section in one book. Writing with the task of communicating the intricacies of Linked Data in the context of Digital Humanities to an interdisciplinary audience runs the risk of oversimplifying to the point of confusion. With this potential flaw in mind, the book deliberately steers away from providing the most detailed and specific technological instructions, wishing to show only enough of the technology so that the reader can assess what is easy, what is hard, what is trivial, what is impossible. Reading this book might not provide all the technological skills required for the implementation of a fully fledged Linked Data project, but it will provide sufficient knowledge to enable those who are new to the method to understand what is happening. Further, it provides them the cognitive tools to critically evaluate the technology, and projects that have opted to utilise it. As such, the relevant technologies will be discussed at a level of difficulty that will, ideally, leave a sense that there are more things to uncover, that there are other, more technical and difficult aspects still to discover.

In Summary

xix

This level of understanding of Linked Data as a technology mirrors in many ways the thoughts and values of the Digital Humanities community, especially when it comes to computer science skills such as programming, writing code, and developing software. Although the community has not yet reached unilateral agreement on the issue, James Gottlieb’s comments are compelling: You can’t be a digital humanist if you don’t understand the digital. That doesn’t mean you have to be able to code any more than being a scholar of French literature means you have to be able to write French literature. You just have to be able to understand the nuances of what you’re studying and how you are studying it. Otherwise, how can you properly interpret the results?1 In many ways, this is the idea that has scoped the extent of the technological engagement in this book: aiming to provide details at a level where they give sufficient information to enable critical engagement. Note 1 www.jamesgottlieb.com/2012/03/08/coding-and-digital-humanities/. Accessed 02/05/2022.

Acknowledgements

This book has been written on the traditional lands of the Ngunnawal and Ngambri. I acknowledge the Traditional Owners and Custodians of the lands on which I work and live, and pay my respects to Indigenous Elders past, present, and emerging. Sovereignty has never been ceded. Canberra sits on what always was and always will be Aboriginal land. I owe a huge debt of gratitude to a lot of people. The case studies, examples, and projects that are mentioned in this book are the result of collaborations, and I would like to thank all my amazing co-authors. I want to single out Dr David Weigl, Prof Stephen Downie, Dr Kevin Page, Pip Willcox, Prof Dave De Roure, and Prof David Bainbridge as colleagues who helped inform and develop my thinking and practical approaches to Linked Data in myriad contexts over several years, but in particular, in the ElePhãT project, discussed in Chapter 3. To Prof Graeme Earl, Dr Nick Gibbins and Dr Mark Weal, who supervised my initial investigations into the world of Linked Data (Chapter 4), and got me hooked on this methodology. Special thanks to Prof Paul Pickering, who deserves a whole paragraph. To date, I have taught the workshop that shares its name with this book at the Digital Humanities Summer School at the University of Oxford, UK, a total of seven times, and attended it once as a student. I want to acknowledge the participants of those workshops, for their questions have enabled and demanded that I inspect and investigate Linked Data from many different perspectives. I want to thank John Pybus, Graham Klyne, and Dr Kevin Page (again!), for delivering that content with me, challenging my approaches where necessary and always providing a robust sounding board for testing out ideas. Thank you also to Megan Gooch and Prof Dave De Roure (again!), for not only making these opportunities for the Summer School happen, but also with the Gale Scholar Asia Pacific Digital Humanities Oxford Fellowship, another perfect opportunity to engage and develop my Linked Data skills and ideas, and to get to know other Linked Data in the Humanities folk. The LAWDI events of 2012 and 2013 were nothing short of fundamental to my academic development as a Linked Data methodologist and gave me a sense of belonging to a global community who were also – like me! – interested and excited about Linked Data. The organisers, funders, and participants to both those events

Acknowledgements xxi deserve my thanks, but I will single out Associate Professor Sebastian Heath and Dan Pett, because they are the ones who made it happen for me. A very special thank-you to Dr Lily Withycombe and Cosi the Aussie, who together delivered on Lily’s promise to read every single sentence. To Dr Katrina Grant, for creating such a collegial and supportive environment in which I wrote this book and indeed all of the time in the Centre for Digital Humanities Research! A heartfelt thank-you to my family for their support and love: Äite ja Iskä, Markka, Sanna, Janne, Pekko, ja tietysti Jeff – kaikki tukevat ihmiset. Uskon, että vuosien varrella kirjahyllyihinne päätyy lukemattomia mun kirjoituksia! Milla, Leah, Nella, ja Hannah – te olette maailman parhaimmat tyypit, ja muistakaa aina, että jos teidän tätinne pystyy kirjoittamaan kirjan, niin kyllä teistäkin jokaikinen aivan varmasti pystyy ihan mihin vaan.

Abbreviations

AI AHRC AR ARC BBC CARE CC CEO CIDOC CRM CITA COVID-19 CSIRAC CSIRO CSV D2RQ DHer EEBO-TCP EPSRC ETCSL EU FAIR FOAF FOMO FOR FRBR FRBRoo GDPR GLAM Go8 GPS GUI

Artificial Intelligence Arts and Humanities Research Council (UK) Augmented Reality Australian Research Council British Broadcasting Corporation Collective benefit, Authority to control, Responsibility, Ethics Creative Commons Chief Executive Officer International Council of Museums (ICOM)’s Conceptual Reference Model Biblioteca Escolar Digital Illness caused by a virus called coronavirus Commonwealth Scientific and Industrial Research Automatic Computer Commonwealth Scientific and Industrial Research Organisation Comma Separated Value D2RQ Platform Digital Humanities Researcher Early English Books Online Text Creation Partnership Engineering and Physical Sciences Research Council (UK) Electronic Text Corpus of Sumerian Literature European Union Findable, Accessible, Interoperable, Reusable Friend of a Friend Fear of Missing Out Field of Research (for the ARC) Functional Requirements for Bibliographic Records FRBR-Object-Oriented General Data Protection Regulation (EU) Galleries, Libraries, Archives, and Museums Group of Eight Global Positioning System Graphical User-Interface

List of Abbreviations xxiii HASS HTDL HTTP HTTPS IBM IFLA IFLA J-DISC JISC JSON JSON-LD KR LGBTQIA+ MODS MADS MySQL NEPOMUK NKOS OCR OWL PARADISEC PDF PR PROV-O QS RDBMS RDF RDFS RO SIG SPARKLIS SPARQL SQL STEM STEAM SVO TEI TTL URI URL URN

Humanities, Arts, and Social Sciences HathiTrust Digital Library HyperText Transfer Protocol Hypertext Transfer Protocol Secure International Business Machines Corporation, a technology corporation International Federation of Library Associations and Institutions LRM IFLA Library Reference Model Online Jazz Discography Joint Information Systems Committee JavaScript Object Notation JSON for Linking Data Knowledge Representation Lesbian, Gay, Bisexual, Transgender, Queer, Intersex, Asexual, Pansexual Bibliographic Metadata Ontology Bibliographic Metadata Ontology (extension to MODS) Open-Source Relational Database Management System Networked Environment for Personalized, Ontology-Based Management of Unified Knowledge Networked Knowledge Organisation Systems Optical Character Recognition Web Ontology Language Pacific And Regional Archive for Digital Sources in Endangered Cultures Portable Document Format Public Relations Provenance Ontology Quantified Self Relational database management system Resource Description Framework RDF Schema Research Object Ontology Special Interest Group Query Builder with Natural Language Processing SPARQL RDF and Query Language Structured Query Language Science, Technology, Engineering, Mathematics Science, Technology, Engineering, Arts, Mathematics Subject-Verb-Object Text Encoding Initiative Terse RDF Triple Language (a syntax and file format for RDF) Uniform Resource Identifier Uniform Resource Locator Uniform Resource Number

xxiv

List of Abbreviations

VIAF W3C WYSIWYG XML Xpath XSLT

Virtual International Authority File World Wide Web Consortium What You See Is What You Get eXtensible MarkUp Language XML Path Language Extensible Stylesheet Language Transformations

Bibliography

Allemang, D., and Hendler, J. (2011). Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL. Elsevier. DuCharme, B. (2013). Learning SPARQL: Querying and Updating With SPARQL 1.1. O’Reilly Media, Inc. Hyvönen, E. (2022). “Digital Humanities on the Semantic Web: Sampo Model and Portal Series”. Semantic Web, 1–16. Juloux, V. B., Gansell, A. R., and Di Ludovico, A. (eds.). (2018). CyberResearch on the Ancient Near East and Neighbouring Regions: Case Studies on Archaeological Data, Objects, Texts, and Digital Archiving. Leiden: Brill. Nurmikko-Fuller, T. (2022). “Teaching Linked Open Data Using Bibliographic Metadata”. Journal of Open Humanities Data, 8. Theocharis, S., and Tsihrintzis, G. A. (2023). “Production and Publication of Linked Open Data: The Case of Open Ontologies”. In Semantic Knowledge Modelling via Open Linked Ontologies. Artificial Intelligence-Enhanced Software and Systems Engineering, vol. 4. Springer. van Hooland, S., and Verborgh, R. (2014). Linked Data for Libraries, Archives and Museums: How to Clean, Link and Publish Your Metadata. Facet Publishing.

1

1.1

A False Dichotomy

Preamble

Auguste Rodin’s sculpture the Gates of Hell is a masterpiece. It can be seen at one of several possible locations worldwide (the three original bronze casts are in Paris, Philadelphia, and Tokyo, with later versions to be found in Zurich, Stanford, Seoul, and Mexico City). It is a colossal piece, six metres in height and four metres in width. It is not as much decorated with, but consisting of almost 200 figures, including the famous The Thinker and The Kiss but dozens of lesser-known ones too, such as Ugolino and His Children. Some of them are small (15 cm), and others are a metre or so. Several different stories are played out in the Gates, all within their own space contributing to a cohesive (albeit chaotic and sporadic) surface of the clearly defined structure, one which is internally multifaceted and diverse. The shared narrative that binds them beyond the Gates is that all refer to the original context of Dante’s Inferno. All this internal complexity, which plays across two halves of one whole, makes the Gates of Hell an excellent metaphor for the interdisciplinarity of Digital Humanities. Those engaged in the practice of Digital Humanities regardless of their respective methodological approaches may chuckle at the thought of the Gates of Hell as a metaphor for the discipline. What sins, passions, and grievous lapses in judgement led into the Hell that is interdisciplinary research? Perhaps the sentiment of abandoning all hope will ring true for some, at least at one time or other. But it is the heterogeneous mix of characters and literary motifs confined within the whole of the sculpture that makes for an ideal visual representation for various granularities of the Digital Humanities. The figures (which differ in shape and size) speak to the way research in the field manifests as myriad individual projects that can (and do!) vary in scope and size; the topics and motifs represent the diversity of domains and methodologies represented and engaged with; and whilst this one monolithic thing consists in fact of many diverse things, these are nevertheless brought together through a cohesive shared context. Since the book is about Linked Data in the Digital Humanities, it might make sense to focus on one figure alone (naturally the analogy for this methodology would be The Thinker!), but rather than engage in such a myopic investigation from the beginning, it is necessary to understand the bigger picture – the wider context – in which the book sits. DOI: 10.4324/9781003197898-1

2 A False Dichotomy At its simplest level, the Gates metaphor serves to illustrate that two parts are needed to make one meaningful whole: the gate itself consists of two doors that meet perfectly in the middle. In the context of Digital Humanities, it is the two academic disciplines of the Sciences and the Arts, which might seem as the unlikeliest of bedfellows and in so many ways worlds apart, which nevertheless come together perfectly. 1.2

Interdisciplinarity

Throughout the years, researchers in the Digital Humanities have repeatedly engaged in what is the seemingly unachievable task of succinctly and absolutely defining the discipline.1 Why do we as a discipline have such a need to define the field, over and over? Or to put it another way, why does Digital Humanities as a field defy definition? It has, after all, existed as a “discipline in its own right” (Schreibman et al., 2004: xxiii) for almost two decades. In that time, it has undergone much growth, diversification, and expansion. It has become more widely recognised, at least in North America and Europe. It clearly sits at the intersection of Humanities disciplines and computational technologies (Schreibman et al., 2015; Svensson, 2010; and Burdick et al., 2016; Zeng, 2019), although the dividing lines can seem somewhat arbitrary at times (archaeology is in Humanities, palaeography is in the Sciences), whilst at others, the application of methodologies from one risks constituting poorly constructed research in the other, especially where the researchers applying these computational methods appear to suffer from hubris and a lack of expertise in the Humanities field. The use of facial-recognition software on portraits, for example de la Rosa and Suárez (2015), is harshly condemned by Bishop (2018: 124) with the words “only to someone entirely unfamiliar with modernism would this [research finding] come as a surprise”. The issue here is that as a truly interdisciplinary field, Digital Humanities incorporates (or has the potential to incorporate) any and all the myriad sub-disciplines that make up the behemoth domain of Humanities, Arts, and Social Sciences (HASS). Add to this all the possible methodologies and approaches from Science, Technology, Engineering, Mathematics (STEM) as well. The possible number of combinations makes the future directions of Digital Humanities investigation increase exponentially in all directions at once. Even just focusing on the Humanities alone, we find seemingly endless different directions for investigation. This is unsurprising, since this collection of disciplines covers vast diversity, the intellectual and academic equivalent of the Tropical Andes. Humanities scholars engaged in topics that range from examining pictographic cave paintings left by the earliest of our ancestors to evaluating portraits of human faces created by artificial intelligence (AI) algorithms. The question then arises: In a field that collectively maps and discusses domains and topics that together cover the entirety of the human experience, what unites us and gives a sense of comradery? Is it the shared struggle of wrangling the ambiguous, messy, and incomplete data (discussed more closely in Chapter 3) that characterises HASS subjects? That, and the absolute fundamental

A False Dichotomy 3 recognition of the importance of critically evaluating the role of interpretation in analysis. Interdisciplinarity is, like Digital Humanities, a concept that seemingly defies succinct definition. There are musings over the boundaries between inter-, multi-, and even trans-disciplinarity. It is difficult to discuss transdisciplinarity as anything but the features shared across all academia: the need for digital infrastructure to enable students to submit their work; the policies that determine the number of international scholarships, and so on. But for inter- and multi-disciplinary ideas, the differentiation and the delivery of their promise is harder still. There may be a temptation to succumb to the types of non-definition as made famous by Potter Stewart (an associate justice of the Supreme Court, in the context of defining obscenity, specifically in the context of a film accused of containing pornography): “I shall not today attempt further to define the kinds of material I understand to be embraced within that shorthand description, and perhaps I could never succeed in intelligibly doing so. But I know it when I see it”.2 Perhaps we can do one better. Arguably one of the simplest and most compelling analogies was first presented in Repko and Szostak (2020): multidisciplinary is like fruit salad. The dish is undeniably a whole, it serves a purpose, it is clear in function, it is greater than the sum of its parts. . , but those composite parts are nevertheless separate. But beyond that, the specific parts of the salad are also changeable. A fruit salad will still be a fruit salad even if this particular one doesn’t have apples in it. Similarly, it is possible to take out all the chunks of oranges, but it is still a fruit salad. And, each component keeps itself a separate entity, even if consumed together. Academic panels are a good example: the panel is multidisciplinary, consisting of representatives from many different areas, with specific aims, paradigms, areas of expertise, and so on. The next example is one of interdisciplinarity, and here the metaphor is of a fruit smoothie. There are many ingredients, all initially changeable, but, ultimately, in a blended drink, there is no way to remove a component. Both the strawberry and the banana are equally important, equally essential, and equally inherent in the composition of the smoothie. They have been broken down, and merged, fully mixed to such an extent that it is impossible to tell where one begins and the other ends. This approach has been the case with the skills and information acquisition among researchers in many successful Linked Data-driven Digital Humanities projects. Interdisciplinarity should be unequivocally supported in research, teaching, and in every committee and panel. Many in the academy have been sold on the benefits of interdisciplinarity for research, at least in theory: certainly, there are funding opportunities and research council prerequisites to illustrate interdisciplinarity in investigatory teams. In the pedagogical context, it might not be quite as universally embraced, and indeed interdisciplinarity may be easier to introduce to postgraduate students who have a solid grounding in one home discipline. That having been said, interdisciplinary built into pedagogy can improve the delivery of authentic assessment (Nurmikko-Fuller and Hart, 2020). For example, although Reeves et al. (2002) do not explicitly use the term, their criteria for success learning ticks many of the boxes of interdisciplinarity, such as asking

4 A False Dichotomy students to examine the task from different perspectives using a variety of sources, and having learning objectives and deliverables that consist of things that can be integrated and applied across different subject areas, leading to what they call “beyond domain-specific outcomes”. Yet, at the very heart of interdisciplinarity is conflict. Anecdotally, there are stories of researchers and academics from different disciplines losing their tempers trying to work with colleagues from others, but those types of personality-driven project-internal struggles are ones that anyone with experience of working in any team will recognise. What is specific to interdisciplinary teams, such as those in the Digital Humanities, is that projects have participants from at least two broad disciplines (e.g. Computer Science and the Humanities). Conflict can arise because the matrices for evaluating success are very different in these two fields, and that, in turn, affect what each member sees as a success, and what their aims and agendas are. And those can be mutually exclusive. A simple example of this might be something like the publication paradigm: computer scientists publish in a space and academic tradition that is more likely to produce co-authored papers that are published in proceedings of carefully ranked and indexed conferences. Their colleagues in the Humanities might labour alone over a single manuscript for years. In some ways then, comparing academic success between different fields is like comparing apples and oranges. Understanding Digital Humanities is understanding the differences between STEM and HASS, and the effect they have. STEM and HASS are categorised not only as two separate things, as falling to the opposite ends of the spectrum, as far apart as can be possible. Again anecdotally, interdisciplinarity has been described as sitting on two chairs simultaneously, but the field is much more dynamic than that. If we must keep to the analogy of sitting down in some form, then Digital Humanities is closer to trying to ride two galloping horses, at all times hoping they do not choose to bolt in different directions. But even that analogy fails: the horses are too clearly defined, tangibly different things. Could we then describe Digital Humanities as a field that defines itself as sitting at the intersection between two completely different things? We could, and often do, at least in the vernacular of the discipline. But that dichotomy is a false one. And not just in Digital Humanities, but as a general axiom. Nowhere is still more evident than in the interdisciplinary spaces where these two clearly and unequivocally overlap. There are a number of different domains, many of which have established themselves as disciplines in their own right, manifesting through topic-specific publication venues such as journals, conferences, academic centres and departments, professorial roles, and so forth. By no means an exhaustive list, these disciplines count among themselves the likes of Digital Humanities, Web Science, Cybernetics, Digital Scholarship, Humanities Computing, Computational Linguists, Digital Design, Digital Musicology and Music Information Retrieval, Distant Reading and Text Analysis, Library and Information Sciences, and so on, indeed there is an entire acronym combining them all – STEAM3 – which has enjoyed sporadic and occasional popularity. Could we surmise that there are no fields left in all of HASS that are absolute in their refusal to engage with any digital

A False Dichotomy 5 technologies? Are there any (broadly defined) computational methods that are yet to be applied to the study and analysis of HASS data? Not many, if any. Among researchers of the Humanities, there is sometimes a notion of a selfproclaimed luddism. It is worn sometimes as a badge of honour, and always forms a part of the individual’s identity: “I’m not good with computers” they mutter, often whilst editing the audio and the visuals of a video they recorded on their mobile device to send to a satellite sitting in space so that it can immediately appear on the screen of some unknown user on the other side of the planet. Tagged, naturally, with internet slang and acronyms that illustrate the idiolect of someone belonging to the global community that calls the Information Highway that is the World Wide Web home. “Luddite” is a perhaps not entirely un-derogatory blanket term for people who do not like, enjoy, or feel particularly confident in the use of digital and computational tools and platforms. The term has its origins in a secret society (of types) of 19th-century England, amongst (in particular) textile workers, who abhorred the introduction of the mechanised looms in the wake of the Industrial Revolution. The conflicts did become bloody, even fatal, and resulted in the penal transportation of some identified members, but over time the label of “luddite” has become diluted and generalised to apply to a rather more innocent type of dislike of computers. Amongst some Humanities researchers, this label is an attractive one: it is a method of asserting identity, of being of something more pure and traditional, of perhaps adhering to the good old ways, the proper intellectual work. And with that comes an insistence (no matter how false) of a lack of skill or ability. But do our colleagues from Computer Science do anything as a regular user that colleagues in the Humanities do not do? How much of what we observe in differences of skill can be just as well interpreted as different degrees of confidence or a badge of identity? And why is this expectation of skill and knowledge one that flows only from the Humanities to Computer Science? Or to put it another way, how often do Computer Scientists consider carrying out their research whilst engaging with Humanities methods such as historical criticism, or with the aim of situating their research into a robust framework of, say, socialist feminist theory? 1.3

Snow’s Two Cultures

There are undeniable differences in the ways these two broad disciplines of HASS and STEM approach research and scholarship, how (academic) achievement is measured, and thus what, in essence, success looks like. In this context, the Gates may not be a perfect analogy: yes, the doors form one cohesive whole, but the differences between the two halves are largely superficial. Underneath is all, the doors are structurally identical. But is this not true of STEM and HASS as well? How deep do these contrasting features need to reach in order to constitute a fundamental difference? What is illustrated here is that the differences we see between fields of STEM and those of HASS are, essentially, behavioural differences within a shared academic landscape that nevertheless adheres to a set of universal rules, where the

6 A False Dichotomy universe as it is understood is academia internationally. These different approaches are caused and perpetuated by both internal and external factors and perceptions. Perhaps it is for this reason that navigating this particular research terrain can be such a hellish task. The idea is nothing new, nor unique: Snow published on the topic in the mid- to late twentieth century (Snow, 1961) and so prolific is the insistence of academics to come back to the idea that the Two Cultures has its own Wikipedia entry.4 The section of this essay that has most often and most consistently captured the attention and the imagination of audiences over the decades is a comparison of domain expertise and experience, epitomised by familiarity with either the Second Law of Thermodynamics or the works of Shakespeare: it’s classic STEM versus HASS. This juxtaposition of literature versus the hard sciences is a narrative that is very common in epitomising the challenges of interdisciplinary research, and is particularly relevant, as we saw earlier, in Digital Humanities. It is also one that runs throughout Snow’s thesis. But there are other segments too. This one articulates the chasm of interdisciplinary communication: Two groups-comparable intelligence, identical in race, not grossly different in social origin, earning about the same incomes, who had almost ceased to communicate at all, who in intellectual, moral and psychological climate had do little in common that instead of going from Burlington House or South Kensington to Cheldea, one might have crossed an ocean. In fact . . . much further than across an ocean – because after a few thousand Atlantic miles, one found Greenwich Village talking precisely the same language as Chelsea, and both having about as much communication with M.I.T. as though the scientists spoke nothing but Tibetan. Setting the question of interdisciplinary communication aside for a moment, the quote deserves some unpacking and a comment or two in terms of being a product of its time. We might want to exclaim about the increased diversity of academia, for example, citing examples of prolific and prestigious professors and leaders who represent minority groups. The truth is that improvement in this regard has been incredibly slow over the last half century. BBC describes the rate of progress as “glacial” and points out that in January 2021, only 1% of professors in the UK were black.5 What was true in 1961 is true in 2021: especially in terms of more senior roles, academics (lecturers, professors) in both STEM and HASS are in the institutions of the Global North and Western societies racially homogenous. This is true of Digital Humanities as well. Two other specified characteristics remain to be discussed: equal pay and social origin. Much has been written about first-generation academics (Ives and CastilloMontoya, 2020 provide a systematic review), but the status itself is not universally recognised or explicitly addressed. In a recent (2021) study of Australian higher education, Patfield et al. interviewed almost 6,500 students enrolled in government schools in the territory of New South Wales. Their findings illustrated that

A False Dichotomy 7 the national education policy, which focused on six groups (including Indigenous groups, learners from low socio-economic backgrounds, students in regional or remote areas, those with disabilities, non-native English speakers, and women in non-traditional fields), was insufficient for not explicitly addressing the challenges of first-generation academics. While change may be afoot, much still needs to happen before we can confidently dismiss Snow’s description of the socio-economic consistency of the background of most academics, be they of STEM, HASS, or any of the myriad disciplines in between. In terms of pay, the prevailing assumption is one of the supremacy of Science over the Humanities, but recent publications have illustrated that the truth is more complicated. A 2015 study by Emolument (a pay-data website),6 which recorded academic salaries from the UK and the USA, found that whilst in the early stages of their careers, STEM graduates earn up to 29% more than their HASS counterparts (in the USA; in the UK the equivalent figure was 6%), by the 15th year of their career, Humanities graduates were earning 7% more than their peers with a degree in the sciences. In this regard, there are no discipline-driven differences that would be maintained throughout an entire academic career – perhaps issues of promotion and salary are examples of transdisciplinary issues. What does remain clear is that research funding is allocated in very different ways: in the UK, the Engineering and Physical Sciences Research Council (EPSRC) had an annual budget of £898 million in 2015–20167 – the equivalent sum for the Arts and Humanities Research Council (AHRC) for the same year was only £98M.8 But how does this affect Digital Humanities? Could it be that the success of Digital Humanities in the UK (e.g. King’s College London, for example, is considered to be a globally leading institution) has been helped and aided by the division of funding councils along these disciplinary lines – perhaps this opens up greater numbers of opportunities? To take my interdisciplinary PhD as an example, I was able to apply to both the EPSRC and the AHRC – and was offered grants by both. An interdisciplinary project in Digital Humanities was seen in the UK at least at the time as desirable, and more crucially fundable, by both parties. This is not a universal pattern. We can contrast it with the Australian setup: there is just one, monolithic Australian Research Council (ARC). Here too, there are differences between the ways funding is allocated between STEM and HASS – but this discrepancy in funding is accredited to a difference in the culture of applying for grants. In 2021, the ARC noted that the “success rates across the range of fields of research the ARC funds are reasonably consistent. While a far greater percentage of STEM applications are received, the success rate is fairly similar at approximately 21 per cent since 2012”.9 Between 2011 and 2015, some 47,000 applications were submitted from the STEM fields, with fewer than 17,000 from the HASS disciplines. The categorisation of Digital Humanities projects is at the discretion of the scholar, since predetermined Field of Research (or FOR) codes are used to classify the proposal and find reviewers. There is no specific FOR code for Digital Humanities in particular, although recent years have seen the development of several specific ones (e.g. Digital History). The problem this presents is that, perhaps rather counter-intuitively, these divisions splinter an already diverse

8 A False Dichotomy field even further. It also means that comprehensively interdisciplinary research agendas become very challenging to establish: Digital History, for example, would be a subset of History, and thus reviewed by Historians according to the criteria and parameters of their discipline: what we want is a main dish of History, with a side serving of the Digital.10 In terms of absolute dollars awarded for research, there is a notable difference in both the total sum of funding and the value of an average grant across the STEM/HASS divide. To illustrate the point, let’s refer again to the ARC. The Grants Dataset11 details the University Group (i.e. be they Group of Eight Universities,12 Innovative Research Universities, or Not Aligned Universities), the Administering Organisation, the State/Territory, Project Code, Program, Scheme, and two FOR13 codes for each successful grant, regardless of type, between 2002 and 2021. The Dataset divides the grants into HASS, STEM, and Not Specified. Using the ARC’s internal categorisation, we can see that within that ten-year period, there was a notable difference in the number of successful grants as well as the average funding amount awarded per successful grant between STEM and HASS. Between 2002 and 2021, the total number of HASS grants awarded was 7,703. These were awarded across 17 different schemes.14 The total sum of the grants was $2,667,319,642. The average value of an awarded grant is $346,315. As there is no way of retrieving Digital Humanities projects (in the absence of a specific FOR code), it’s not possible to reflect on the funding of the field as an entity. For STEM, the numbers are larger across the board. There have been 21,668 successful grants over the period of 2002–2021. That’s roughly thrice as many as in HASS – this reflects the earlier claim that the rates of success have been largely consistent: there were almost three times as many applications from STEM disciplines than from HASS, so it stands to reason that with the similar rate of success, thrice as many grants would be successful as well. This greater number of applications also shows in the vastly larger overall sum of dollars awarded to STEM research by the ARC over this ten-year period: the total sum was $10,616,548,122. Here, the pattern of thrice no longer holds: the total sum of money awarded for the sciences is five times as high as that received by the Humanities and Arts. This reflects on the average size of a successful grant: for the STEM disciplines, the value is some $140,000 higher, averaging at $489,942 per grant. The funding was awarded across 19 distinct schemes (two more than for HASS).15 There is thus a twofold difference in the way STEM and HASS researchers apply for funding: STEM researchers apply more frequently and in greater numbers, and, second, they ask, on average, for larger grants. The former is undoubtedly the result of a history of funding: STEM has received more funding so has been in a position to employ more researchers who then in turn are expected to apply for funding that creates more positions for new STEM researchers to fill. In this aspect, are the HASS disciplines missing out on a trick? It boils down to there being two different types of academic culture. Many would generalise the work of a Humanities scholar as that which is typically carried out in isolation, whilst STEM roles are often built into labs and

A False Dichotomy 9 research clusters. Is the number of Humanities scholars low because the culture of grants is one of supporting individual endeavour rather than establishing research clusters around a lead investigator? In other words, are there at least seemingly fundamental differences between the broad categories of research manifestation of different cultures of applying? Perhaps different cultures of research, and a largely different set of expectations? How then do we successfully map and measure achievements in a research landscape when such differences permeate the process from the outset? And perhaps most importantly, which of the two cultures is the one for Digital Humanities? To return to Snow’s quote, we can see that since the 1960s, the academic landscape has both changed and remained the same. The academic population is still largely homogenous in terms of race and socio-economic background – disciplinary differences do exist in terms of income and funding but these hark back to cultural differences not between individuals but between the behemoth disciplinary classifications of STEM and HASS. In their 2015 publication, Bod and Kursell note that whilst strides have been made in both camps to recognise similarities in infrastructures and methodologies, many of the “historiographies of the sciences and the Humanities seem to persist as two separate fields” (p. 337). In our collective quest to understand where we are at – so as to know which way the future lies – we look back on the history of our disciplines, but do so in ways that perpetuate the divide between Us and Them, defining the history of STEM and HASS by way of excluding the other. These cultural differences of paradigms of publication, research agendas, funding models, and exclusive histories currently perpetuate the chasm between these two areas, and complicated communication between them. But for what purpose and to whose benefit? Arguably, neither side has more to gain from this dichotomy than they do from the aggregation of skills and knowledge from both sides. And why should research be split into either HASS or STEM in the first place? What purpose does it serve and what tangible benefits are to be reaped from it? All this serves to remind us that in relation to Digital Humanities there is no chasm between STEM and HASS. Digital Humanities research can – and has been – funded by different research councils depending on the individual project and not according to a pre-existing disciplinary pigeon hole. The Digital Humanities Department at King’s College London provides an excellent example of this.16 and clearly underscores the interdisciplinary nature of the field. The Department at King’s received almost £5,145,000 from the EPSRC to fund eight projects over a period of 16 years starting in 2008 and almost £13,660,000 from the AHRC to fund 64 projects 2000–2022. Drilling down into the data from 2012–2023 further highlights several important issues in relation to scale. Looking at those awarded between 2012 and 2022, we can see that the AHRC funded 31, while the EPSRC awarded just 6. Having said that, the EPSRC awarded larger sums in terms of both the total allocation and the average amount per grant than the AHRC. The total value of grants from the AHRC was just over £6,800,000 (with an average grant size of £220,000); whereas from the EPSRC, the total was over £4,600,000 (with an average grant of £777,000). Moreover, there was a marked

10 A False Dichotomy difference in relation to the numbers of recipients in the grants themselves. Almost half (45%) of those awarded by the AHRC went to single applicants, with another 32% going to investigatory teams of just two. In sharp contrast, half of the successful applications to the EPSRC were awarded to teams of six or more applicants. Even more significantly, all of the highest-grossing grants from both the AHRC and the EPSRC were awarded to teams of multiple investigators. In fact, all grants with a value of more than £1,000,000 awarded by both research councils were awarded to teams (and supports the argument regarding co-authorship discussed in Section 1.2). 1.4 The Role of Humanities in Computer Science The public face of the Digital Humanities has often been dominated by literature and text analysis. So much so, that an axiom persists in the discipline that the first-ever Digital Humanities project was the Index Thomisticus, a collaboration between IBM and an Italian Jesuit priest Father Roberto Busa, in the 1940s and 1950s. It was a project of computational linguistics, indexes, and computergenerated concordance. But it is possible to argue that this origin story is flawed, because we can show that HASS has been a part of the development of computers, digital platforms, and hypertext systems since the beginning. There have always been interdisciplinary thinkers, much before we coined convenient descriptive labels to categorise them. One example comes to us from Iraq a millennia before the invention of digital electronic computers. As I described in the 2023 piece for the Conversation,17 when Europe was in the midst of the so-called Dark Ages, interdisciplinary thinkers collaborated in what is known as the “House of Wisdom” (Bayt-al Hikmah) in Baghdad (Al-Khalili, 2010). It was a centre of translation, learning, poetry, engineering, theology, and myriad other subjects: it was a centre for what we could now categorise as pioneering inventions in science, technology, and engineering, all carried out by poets, musicians, philosophers, linguists, and librarians. For example, in 850 AD, the Banu Musa brothers wrote and published a work known as the “Book of Ingenious Devices”, which describes automata and other precursors to modern robots, and what has even been argued to be the world’s first programmable machine (Koetsier, 2001). Another scholar of the House of Wisdom at the time was Mohammad ibn Musa al-Khwarizmi, whose name inspired the terms “algebra” and “algorithm”. Al-Khwarizmi worked closely with al-Kindi, an Abbasid polymath who was both a student of cryptanalysis and a pioneer in music theory. The House of Wisdom was a centre of interdisciplinary thinkers establishing the foundations of the types of mathematics and engineering that have been instrumental in the development of Computer Science. Music gives us another perfect example, for it has been part of the history of mechanical computers since the very beginning. The earliest of examples comes from the worlds of Ada Lovelace, widely considered the first computer programmer in recognition of her Notes18 for Charles Babbage’s Analytical Engine, published in 1843. The Notes were written as an addition to the work of translating the article

A False Dichotomy 11 written by Luigi Menabrea: Lovelace’s comments ended up being triple the length of Menabrea’s article, and are now described by the Mathematical Association of America as “a classic in the early literature on computers”.19 There are those who seek to critique, down-play, or dispute Lovelace’s contribution to Computer Science (e.g. see Misa (2016) for a summary of work in this vein). But if nothing else, Ada’s family background makes for a fascinating case study example for the Nature versus Nurture debate. Ada was the only legitimate daughter of the infamous poet Lord Byron, and her mother (who was also a mathematician), in an attempt to protect her from her father’s detrimental influence, encouraged her to discard the arts and pursue mathematics and logic exclusively instead. This did not prevent Ada from being subject of gossip regarding extramarital affairs, or end up being what turned out to be a very poor20 gambler indeed. But more significantly, she was undoubtedly an interdisciplinary thinker. The signs of what we would today consider to be interdisciplinary thinking are clear in her Notes (as published in Misa, 2016): Supposing, for instance, that the fundamental relations of pitched sounds in the science of harmony and of musical composition were susceptible of such expression and adaptations, the engine might compose elaborate and scientific pieces of music of any degree of complexity or extent (Note A, p. 694) and less specifically “the Analytical Engine weaves algebraic patterns just as the Jacquard-loom weaves flowers and leaves” (Note A, p. 696; Lovelace refers to the “art of weaving” in Notes C). Thus, in her processes of making meaning of the potential of computation, of understanding algorithmic concepts and their applications, and in communicating her ideas about the potential of the Engine to others, Lovelace referred to notions of music, and art. If not this, then what could be the earliest-known example of what we now refer to not just as interdisciplinary but also as specifically Digital Humanities thinking? Lovelace was not the only one to see the potential between these new machines and music, and the latter is a feature of the history of digital computers. The Commonwealth Scientific and Industrial Research Automatic Computer (CSIRAC) was among the pioneers of stored-programme computers, and Australia’s first digital one – and almost certainly the first anywhere to play music (Doornbusch, 2004). Whilst myriad reasons, research agendas, and so on are likely to have contributed to the development of CSIRAC, simply the thought of testing whether a machine such as this could play music illustrates the ubiquity of Humanities subjects across the diversity of the STEM disciplines as well. The crucial difference here is that it is a Humanities subject matter, and not a methodology, which plays a role. Beyond these origins of mechanical, electronic, and digital computers as hardware, Humanities data (the subjects of research and investigation in the Humanities, such as literature, music, and art) has also played a major role in the development of hypertext systems: Prof Dame Wendy Hall has accredited her work in the 1980s with the archive of Earl Mountbatten as eventually leading to the development of

12 A False Dichotomy Microcosm21 (an early contemporary to the Web), and Dallas (1992) outlined his suggestions for semantic components and logic programming (with a case study of figurative art on Classical Attic funerary markers) nearly a decade before BernersLee’s et al. (2001) article launched the concept of semantic technologies to the masses. 1.5

Linked Data and the Humanities – a Perfect Pair?

Having these ideas of the interdisciplinary of the Digital Humanities in our minds, it is time to address the second major part of the title for the book: Linked Data. In early 2021, Miriam Posner described Linked Data as “not really one key piece technology, but a set of best practices for publishing data on the Web”.22 She is right that there is no one key piece of software that is used to create Linked Data, but there are many different methods, workflows, tools, and so forth. And yes, it is a process for data publication. But it is more than that. It is a specific methodology, and a standardised one at that. Linked Data is a way of doing things, namely, the publication of information online in accordance with internationally specified standards using existing Web technologies and architecture. It is an information publication paradigm. And it’s not a matter of best practice, per se. The quality of the Linked Data is measured by its adherence to the Five Star Standard. This standard, which has been defined by Tim Berners-Lee, was published by the World Wide Web Consortium (W3C) in 2013.23 It is depicted and expanded on in Table 1.1.24 Note that there is no set software, no particular details as to how each stage of Linked Data quality is to be reached. Table 1.1 Five Star Standard of Linked Open Data Quality

Description

Example

☆

Data is available on the Web, in any format. Data is available as machine-readable, structured data, but in a proprietary format. Data is available as machine-readable, structured, and nonproprietary format.

PDF of a dataset (not machine actionable, but viewable by human users). A tabular dataset is made available as an MS Excel spreadsheet, which requires anyone wishing to access the data to have that specific software installed. A tabular dataset is made available as a CSV file, which can be used with many different software and many programming languages can be used to read it. Data has been converted to RDF and it can be queried using SPARQL. Data published as RDF has external links to other Linked Data resources or authority files such as VIAF.org or Wikipedia.

☆☆

☆☆☆

☆☆☆☆ ☆☆☆☆☆

Data is published using W3C open standards. Data is connected to other Linked Data online.

A False Dichotomy 13 Put together, we can see the (at least seemingly) ideal pairing of the Linked Data methodology with Humanities datasets is based on a simple premise: humans are network thinkers, and Linked Data is a network technology (Nurmikko-Fuller, 2018). The parallels between the two run wide and deep. Even the terminologies we used to describe the concepts of Linked Data echo earlier Humanities thought and analysis. 1.5.1

The First Wittgenstein and the RDF Triple

In his opening statement to Tractatus Logico-Philosophicus, Wittgenstein (1922) quotes Kürnberger to say that “whatever a man knows . . . can be said in three words”. Similarly, one of the cornerstones of the Linked Data approach is that all knowledge can be captured in statements of three components. We call this the RDF (or Resource Description Framework) triple. RDF is a W3C specification. It is not a specific piece of software that enables one to implement a Linked Data project, but rather an abstract data model that enables us to publish data in a machine-navigable format. There are two key considerations for understanding RDF from a technological perspective. First, RDF is mostly expressed in triples, which, as the nomenclature would suggest, consists of three parts. These parts are called the Subject, the predicate, and the Object. At least the Subject and the predicate must always be a URI, the Object can be a URI, a string (of letters), or an integer (numbers). If the Object of one triple is a URI, it can be the Subject another triple, thus resulting in an interconnected graph, which when visualised often looks like a network diagram of arcs and nodes: we consider the Subject and Object to be data entities, and the predicate between as a relationship, that connects the two and gives them meaning. The Subject-predicate-Object layout lends itself very well to the sentence structure of a subject-verb-object (SVO) language, such as English. By way of a simple example, we could state a fact: William Shakespeare wrote “Romeo & Juliet”. The Wittgensteinian pattern of three in adherence to the SVO layout is clear: the subject (William Shakespeare) wrote (verb) “Romeo & Juliet” (the object). And this pattern would lend itself to representation as an RDF triple: Subject (William Shakespeare) predicate (wrote) Object (“Romeo & Juliet”). In this very literal and somewhat oversimplified sense, facts can be expressed in RDF in the equivalence of three words. The RDF triple is often visualised as a minute network diagram. The Subject and Object are the nodes (often depicted as dots or circles); the predicate is an arrow (a directed, labelled line) running from the Subject to the Object. Examples of these visualisations are readily available.25 The step that many beginners find the biggest leap is that rather than these human-friendly diagrams of circles and lines, RDF is expressed explicitly through Hypertext Transfer Protocol (HTTP) URIs. This might seem suddenly very heavy in acronyms, but we’re all very familiar with them as the location of pages on the Web. Some browsers now hide the prefix, but all pages on the Web have a distinctive identifier that can be seen in the address bar of the browser. And they all start

14 A False Dichotomy with HTTP://. Where Linked Data differs from this current Web of pages and sites is that these HTTP URIs point to data instances, and the relationships between those data instances. The Web of Documents is a hypermedia system based on hyperlinks that connect disparate resources online. Each resource sits at a specific location, captured as a unique identifier. Unique in the context of the entirety of the whole Web. No two pages can have the same address, or the process of retrieving that content and displaying it to the user in the browser would fail. The technical specifications of these different types of identifiers have been published many times before (Wood et al., 2014, is a good example), which is why they will not be the focus here. There are essentially three types of universal identifiers on the Web: the URI (uniform resource identifier), URL (uniform resource locator), and URN (which refers to numbers). URLs and URNs are often considered to be equal but different subsets of URIs, but in the case of the Linked Data paradigm in particular, the URI is rather more uniquely specific. They are used to point not to existing digital resources, but information entities and the relationships between those entities. On the current Web, URLs are addresses where websites sit. We click on a link, and are expected to be taken to a page, an image, an audio file, or a video. URIs in Linked Data projects on the other hand, if clicked, are likely to return a 404 error code: there is often no digital resource (no “thing”) that it points to. This is not a failure of the system or an example of the tech axiom or adage of “a feature not a bug”. Rather, this is an example of the ways in which URIs are used to represent both the underlying database structure and the data that populates it. In the Linked Data context, we can confidently say that Wittgenstein had it right a century ago, but we need to update the quote to say “whatever a man knows . . . can be said in three URIs”. 1.5.2

The Second Wittgenstein and Explicit Statement of Facts

Wittgenstein’s proposition 1.1 is that “The World is the totality of facts, not of things” (1922: 5). This concept is fundamental to Linked Data projects: RDF triples are created to specify absolute and unambiguous relationships between unambiguous entities. So absolute is the adherence to unequivocal facts that there is a specific assertion regarding it, or its absence: The Open World Assumption takes that a statement can be true even if it is not explicitly known to be true. Linked Data methods and Semantic Web tools such as the OWL have this assumption built into them. The practical outcome of this is that it is possible to represent statements which are inexplicit, or have yet to be explicitly proven, and, as long as the graph adheres to its own internally consistent logic, it is technologically possible to make statements that we do not believe in, or which we cannot prove. Furthermore, since Linked Data is a method of representing digital content, all “things” are intangible. Let’s imagine a simple set of information represented as three URIs. One of them refers to William Shakespeare (author and historical figure), another to a play (“Romeo and Juliet”, for example), and a third to a relationship that defines Shakespeare as the author of the play. In the case of the

A False Dichotomy 15 example of William Shakespeare, the URI (in this case, let’s use http://viaf.org/ viaf/96994048/) does not point to a tangible, real-life person: it is not an identifier for a thing. The URI points to a digital resource that has information about that thing, in this case, William Shakespeare. It is from the Virtual International Authority File (VIAF) and is used to represent the idea of Shakespeare. But why? Because VIAF is an example of an authority file. The URI does not point to a webpage about Shakespeare, nor a picture of him, nor any of his works. The URI has a page that is rendered in the browser, but what it refers to is an internationally agreed-upon identifier for the historical person. The point becomes quite philosophical here. Essentially, Shakespeare is a notion in the universe, which has timelessness to it. He manifested at one time as a (tangible) person, but his physical existence does equate to the totality of his existence. It is to this physical manifestation of a person that we can attach some concrete assertions, such as his name, or place of birth. But the relationship between Shakespeare the author and his work is a persistent truth: he will always be the entity that wrote the play. Creation dates can provide an example that helps differentiate between a tangible manifestation of a person and their abstracted counterpart. If the URI was to point to a webpage, it’s creation date would be in, say, 2022. When a URI points to a notion of a person, we can assert this creation date (birth) as having been in 1564. In some systems, explicitly defined rules may determine which kinds of assertions are possible about different entities. But what determines which kinds of assertions are possible? In the Linked Data world, this is usually accomplished through ontologies. Although the term is borrowed from philosophy, in the realm of Linked Data, ontologies are a specific thing. Perhaps the most famous of definitions for ontologies is the one from Gruber (1993): “An ontology is an explicit specification of a conceptualization”. Ontologies are discussed in more detail in Chapter 4, so for now, we must content ourselves with a simpler explanation. Conceptually, an ontology is a description of a topic, a domain, a universe. They consist of listings of the different entities that exist in that space (such as people, or places, or concepts, or events, or types of pizza), and the relationships that can exist between those entities. If you think that sounds a lot like the RDF triple described earlier, you would be absolutely right. What this simple premise equates to is that any ontology we design ends up representing not just the data but also our knowledge about that subject. It reflects all the information about that topic that has been made available to us, and thus too then, either directly or indirectly, ontologies capture our biases, because they are informed by our reality, or more precisely, our understanding of our reality. But, their primary aim is to capture the facts: that is to say, to explicitly state the existence of the fundamental components of knowledge that exist in the represented topic or domain. More tangibly, an ontology is a file read by a piece of software. It is not complicated technologically: they can be written in simple text-editing software such as NotePad or TextEdit that do not add formatting, hidden code, or hypertext to the file – for this reason, more complex word-processors such as Microsoft Word are not suitable for the task. These files map the domain: to write an ontology is to build semantic models with RDF, which are more specific models than the RDF

16 A False Dichotomy triple or the abstract model itself. We do this to facilitate information exchange and to let others (humans and software agents alike) know what our data is about. It is a way of scaling complexity but also a method for adding meaning to your data so that it becomes discoverable and usable by others. Several other of Wittgenstein’s propositions echo with relevance and familiarity with the abstract data model of RDF and the First and Second Order Logics that we used to validate ontological structures. This is not to say that Linked Data proves that Wittgenstein achieved his aim of “[solving] the problems of philosophy” (Grayling, 1988: 12), but what we can see clearly is that the approaches of Western philosophers and data scientists alike are influenced by the same perspectives for categorising, understanding, representing, and analysing the reality we encounter. It’s almost as if there were not, at the conceptual and cognitive level, such big differences between philosophers of HASS and the data scientists of STEM. 1.5.3

Foucault, Semantic Web, and Properties

The World Wide Web was first launched in 1989. The first time the public was made aware of the idea of the Semantic Web was in 2001. How then could the French philosopher Michel Foucault (1926–1984) know to talk about the “semantic web” and “properties” two decades earlier (Foucault, 1970: 17–18)? Properties (or perhaps more accurately properties) we have seen already, but what of the Semantic Web? Wilks and Brewster (2009: 1) described it as a “more powerful, more functional, and more capable version of [the] document and language-centric Web”. Since then, it has become increasingly synonymous with the “Web of Data”.26 What the Web and the Semantic Web have in common is that both are, simply put, hypermedia systems (or, perhaps, since the interlinking occurs at the level of specific data instances, it would be better described as a hyperData system) concerned with the publishing of information publicly in a shared technological format that enables software agents to infer meaning. If Linked Data is the process, then the Semantic Web is the noun. In fact, Foucault’s terminology is discussing an even older informationstructuring process, dating to the sixteenth century. This use of terminology illustrates the conceptual similarities of the way human beings make sense of the universe: both the words “semantic” and “web” are significant, and capture the idea of meaning-making through connections between things. Foucault goes on further to describe the understanding of ideas using terms which are remarkably reminiscent of the way we describe RDF triples: Thus, by this linking . . . that brings like things together and makes adjacent things similar, the world is linked together like a chain. At each point of contact, there begins and ends a link that resembles the one before it and the one after it; and from circle to circle, these similitudes continue, holding the extremes apart . . . yet bringing them together. (Foucault, 1970: 19)

A False Dichotomy 17 The phrasing conjures up an image of interconnected triples, where each looks the same (consisting as they all do, of a Subject, a predicate, and an Object, each where possible consisting of URIs), and where the Object of one triple constitutes the Subject of another, creating an interconnected knowledge graph. The properties that connect the two capture the connections between things, just as described in the quote above. And who could argue with the parallel of circles and lines when comparing and contrasting them to the ways in which ontological structures and instance-level data are represented in Linked Data papers using diagrams of circles and lines? The borrowing of the term “ontology” by computer scientists from philosophers is perhaps the worst-kept secret of the Linked Data community, and one that can cause confusion in the intersection of Humanities and Computer Science that is the Digital Humanities. As noted by Foucault (1970: 120): For two centuries, Western discourse was the locus of ontology. When it named the being of all representation in general, it was philosophy: theory of knowledge and analysis of ideas. When it ascribed to each thing resented the name that was fitted to it, and laid out the grid of well-made language across the whole field of representation, then it was science – nomenclature and taxonomy. Foucault’s description of human cognition parallels that of Vannevar Bush (an American engineer). As we noted in Nurmikko-Fuller and Pickering (2021), this is clear in the context of Bush’s description of the motivation for the Memex some 15 years earlier (1945: 14): The human mind does not work [in a direct] way. It operates by association. With one item in its grasp, it snaps instantly to the next that is suggested by the association of thoughts, in accordance with some intricate web of trails. And even more recently still, Simpson (2020) summarises explicitly on the parallels between hypertext systems and cognitive processes – the same description can easily be applied to the parallels between graph databases and mindmaps. 1.5.4.

Meaning Derived From Connections

In both the fields of Philosophy and of Computer Science, an agreement exists as to the way meaning is conjured through connections. It should not be surprising then, based even on these two examples alone, that the Semantic Web would manifest in such a way as well. As noted by Simpson (2020, July): “graph-based hypertext . . . provided a better way of representing the interconnected way in which I thought and worked”. It seems the natural fit of technology and thought to utilise the computational process of Linked Data to represent, capture, and query information we as humans have collected about the human world.

18 A False Dichotomy Furthermore, all data contains a degree of ambiguity, a fuzziness at the edges. To this point, Linked Data methodologists have sought ways to eliminate this vagueness; to clean it up and remove it. The absolute unambiguity of URIs has been cited as the ultimate method for identifying and uniquely labelling everything. Instead of eliminating this ambiguity, it should be actively embraced; innovative, disciplinary-neutral methodologies should be developed to purposely capture and leverage ambiguity and methods for incorporating it into automated processes of machine inference should be more deliberately explored. Perhaps unsurprisingly, scholars have done little to address ambiguity and vagueness using Linked Data. An examination of existing ontologies through aggregator projects such as the Linked Open Vocabularies highlights a very limited number of existing examples of knowledge representation systems that address uncertainty. Silvio Peroni’s 2013 work on vagueness would be one such example, but the lack of uptake and widespread use of his ontology is perhaps reflected in the fact that it is no longer available online (the URI returns an all-too-familiar 404 error code). Peroni’s other ontological work (with Shotton, 2018) does not explicitly cover methods for capturing the uncertainty or the incompleteness of data. In other cases where ambiguity and vagueness are represented in ontological structures (such as the OntoMedia ontology,27 which allows a thing to be categorised as an “Unknown”), they are unambiguously captured as information entities (i.e. Class in the ontology). In this way, they essentially represent vagueness and uncertainty to the human user, not at the computational level. The great unfulfilled promise of Linked Data is machine inference, the automated deduction of implicit connections and relationships between explicitly declared facts. It has immense potential for coping with messy data: capturing ambiguity rather than eliminating it. The core of the challenge is thus not simply how to apply powerful Semantic Web (Berners-Lee et al., 2001) technologies to Humanities investigations, but how to develop these technologies to confront the problem of semantic ambiguity within Linked Data. Some two decades ago, Linked Data was identified as a practical method for realising a next stage in the development of the Web: an evolution from a Web of Documents (Képéklian et al., 2015) to a Web of Data (Berners-Lee, 1999). To infer new knowledge from this proliferation of datasets, we must find ways that enable us to capture the importance of and the effect of various degrees and types of uncertainty inherent in them. Although this data is utilised by both people and automated processes alike, until now, there is little evidence of systematic attempts to identify a comprehensive solution, either in the academy or in the industry. 1.6

Conclusion

The chapter began with the setting of a scene for understanding the interdisciplinary world of the Digital Humanities by way of a visual metaphor of Rodin’s Gates of Hell. Myriad sculptures together constitute one larger, cohesive piece. Projects in the Digital Humanities vary in ambition, scope, focus, agenda, funding sources,

A False Dichotomy 19 team size, methodology, data, and desired research outcome. All our projects are different, but there is a clear sense of belonging to a larger whole – a shared context. And yet, the division of the Gates into two doors underlying the sculptures can be seen as representing the binary fields of STEM and HASS. They are parallel, but not identical. Analysing the differences between the two disciplines highlights two things: that there are many differences and that the dichotomy between the two is false! Snow’s Two Cultures theory gives us a way to grapple with that paradox. There are many overarching similarities in STEM and HASS at the level of the greater academy: the homogeneity of high-ranking members of universities, for example. But at the ground roots level, we see evidence of very different cultural approaches and behavioural models. These affect how funding is applied for, how research is carried out, and how results are disseminated. There are clear differences in how success is measured. And yet, this seems like a relatively modern diversion. The origins of computation include clearly interdisciplinary thinkers: Ada Lovelace, the world’s first computer programmer, is a perfect example of someone who recognised the potential of computational machines to make music. Music, arts, and historical documents have influenced not only the birth of Digital Humanities as a field but also the development of computers and hypertext systems, such as Microcosm. The Digital Humanities is inherently diverse and home to many different digital tools and computational methodologies. The flexibility of the Linked Data approach in particular makes it an excellent pairing with the cognitive processes of Humanities scholars and researchers. The network-like representation of ideas, associations, and connections sometimes expressed by mindmaps are an ideal match for the knowledge graphs of interconnected RDF triples that sit at the heart of the Linked Data approach. It’s this shared goal and process of making meaning through connections that make the Linked Data method such an ideal fit for the (Digital) Humanities. And this, in turn, proves that the dichotomy between STEM and HASS is false: both are needed in equal measures, to reach this shared goal. As the lens through which Linked Data is examined in this book, Digital Humanities deserves to be defined and discussed. But has interdisciplinarity itself become the methodology du jour? Perhaps so, but it seems this (relatively) new way of doing things is one that’s here to stay (the Gates are a timeless classic, after all!). Regardless of the disciplinary context, we all live in an era of increasingly sophisticated algorithms mapping, measuring, and affecting our lives through data-driven analytics. The Data Economy (see Chapter 2) impacts the daily lives of an ever-increasing number of citizens. Linked Data offers us ways to capture complexity in data, and even scale it gracefully. But to do so well in an inherently interdisciplinary way, and with thoughtful, critical engagement, we must combine this computationally powerful tool with considerations of human-centred issues of privacy, trust, agency, authority, and ethical engagement.

20 A False Dichotomy Notes 1 Although he may not have been the originator of the Digital Humanities-specific take on “That’s on me, I set the bar too low” meme (to say data provenance with memes is difficult would be an understatement), one of the first to tweet it was Ryan Cordell (@ryancordell) on 01/06/2021. Ryan’s take was: Jake: Jess: Jake: Jess:

“I’m an expert in Digital Humanities” “Oh yeah? Name the top 10 most important Digital Humanities articles” “What is Digital Humanities?” “That’s on me, I set the bar too low”

2 www.mtsu.edu/first-amendment/article/1359/potter-stewart#:~:text=Potter%20 Stewart%20(1915%E2%80%931985),pithy%20prose%20resulted%20in%20a. Accessed 31/01/2023. 3 STEAM refers to Science, Technology, Engineering, Arts, and Mathematics. 4 https://en.wikipedia.org/wiki/The_Two_Cultures. Accessed 03/08/2021. 5 www.bbc.com/news/education-55723120. Accessed 03/08/2021. 6 www.emolument.com/career_advice/sciences_vs_humanities_students_salaries_who_ earns_more#gsc.tab=0. Accessed 24/08/2021. 7 https://epsrc.ukri.org/about/facts/budget/. Accessed 24/08/2021. 8 https://ahrc.ukri.org/documents/strategy/arts-humanities-research-council-deliveryplan-2015-2016/. Accessed 24/08/2021. 9 www.arc.gov.au/grants-and-funding/apply-funding/grants-dataset/trend-visualisation/ ncgp-trends-success-rates. Accessed 24/08/2021. 10 Linked Data projects are perhaps most suitably categorised as 080707 Organisation of Information and Knowledge Resources: It is a nested subset of first Library and Information Sciences, and ultimately Information and Computing Sciences. Although I have no objection to this, I note that the classification is not neutral. 11 www.arc.gov.au/grants-and-funding/apply-funding/grants-dataset. Accessed 21/03/2022. 12 Group of Eight (or Go8) universities refers to a cluster of research-intensive universities in Australia. These are the University of Melbourne, the Australian National University, the University of Sydney, the University of Queensland, the University of Western Australia, the University of Adelaide, Monash University and UNSW (University of New South Wales) Sydney. Like the Ivy League of the United States or the Russell Group Universities of the UK, this group consists of universities which rank highly both in Australia and internationally. For more information about the Go8, see https://go8.edu. au/about/the-go8. Accessed 21/03/2022. 13 FOR (or Fields of Research) Codes are a mode of classification utilised to measure and analyse research in Australia and New Zealand. For more information about FOR and how they fit within a wider classification system, see www.arc.gov.au/ grants/grant-application/classification-codes-rfcd-seo-and-anzsic-codes. Accessed 21/03/2022. 14 These 17 schemes were Discovery Early Career Researcher Award; Discovery Projects; Australian Laureate Fellowships; ARC Future Fellowships; Industrial Transformation Training Centres; Discovery Indigenous; Linkage Infrastructure, Equipment and Facilities; Linkage Projects; ARC Centres of Excellence; Special Research Initiatives; Supporting Responses to Commonwealth Science Council Priorities; Learned Academies Special Projects; Industrial Transformation Research Hubs; Discovery Indigenous Researchers Development; Linkage – International; Federation Fellowships; and, Research Networks. 15 The two additional schemes to award funding for STEM research exclusively are the Super Science Fellowships and the Linkage-CSIRO. 16 https://kclpure.kcl.ac.uk/portal/en/organisations/digital-humanities(b2f7d708-74dd4fbe-b858-a36f316611dc)/projects.html?query=. Accessed 25/01/2023.

A False Dichotomy 21 17 https://theconversation.com/long-before-silicon-valley-scholars-in-ancient-iraqcreated-an-intellectual-hub-that-revolutionised-science-191589. Accessed 31/01/2023. 18 See, for example, https://play.google.com/books/reader?id=EIVqHUm9WlkC&pg=GBS. PA690&hl=en_GB. Accessed 24/08/2021. 19 www.maa.org/press/periodicals/convergence/mathematical-treasure-ada-lovelacesnotes-on-the-analytic-engine. Accessed 24/08/2021. 20 All puns intended. 21 www.museumsandtheweb.com/forum/report_cultural_heritage_and_the_semantic_ web_study_da.html. Accessed 03/08/2021. 22 www.youtube.com/watch?v=VZBpFiLbi-Y&feature=youtu.be&ab_channel=MiriamPosner. Accessed 03/08/2021. 23 www.w3.org/2011/gld/wiki/5_Star_Linked_Data. Accessed 25/05/2022. 24 The table is inspired by other sources such as www.w3.org/2011/gld/wiki/5_Star_ Linked_Data#:~:text=Short%20description%3A%20Tim%20Berners%2DLee,the%20 previous%20step(s) and https://5stardata.info/en/. Accessed 14/02/2023. 25 See, for example, www.w3.org/TR/rdf11-primer/, https://stackoverflow.com/questions/ 69877279/retrieve-objects-from-rdf-triples or www.marklogic.com/blog/making-newconnections-ml-semantics/ for simple examples of the triple visualised as labelled circles and arrows. Accessed 14/02/2023. 26 www.w3.org/DesignIssues/LinkedData.html. Accessed 27/05/2022. 27 www.semanticscholar.org/paper/OntoMedia%3A-An-Ontology-for-the-Representationof-Jewell-Lawrence/c8e6add16ad6efd76ca13135c5fcf808f73cf3c5. Accessed 24/01/2023.

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22 A False Dichotomy Képéklian, G., Curé, O., and Bihanic, L. (2015). “From the Web of Documents to the Linked Data”. In Zimányi, E., and Kutsche, R. D. (eds). Business Intelligence. eBISS 2014. Lecture Notes in Business Information Processing, vol 205. Springer. Koetsier, T. (2001). “On the Prehistory of Programmable Machines: Musical Automata, Looms, Calculators”. Mechanism and Machine Theory, 36(5), pp. 589–603. Misa, T. J. (2016). “Charles Babbage, Ada Lovelace, and the Bernoulli Numbers”. In Hammerman, R., and Russell, A. L. (eds). Ada’s Legacy: Cultures of Computing From the Victorian to the Digital Age. Morgan and Claypool. Nurmikko-Fuller, T. (2018). “Publishing Sumerian Literature on the Semantic Web”. In Juloux, V., Gansell, A., and Di Ludovico, A. (eds). CyberResearch on the Ancient Near East and Neighboring Regions. Brill. Nurmikko-Fuller, T., and Hart, I. E. (2020). “Constructive Alignment and Authentic Assessment in a Media-Rich Undergraduate Course”. Educational Media International, 57(2), pp. 167–182. Nurmikko-Fuller, T., and Pickering, P. (2021). “Reductio ad Absurdum?: From Analogue Hypertext to Digital Humanities”. Proceedings of the 32nd ACM Conference on Hypertext and Social Media (ACMHT21), Dublin, Ireland, 30 August–02 September. Patfield, S., Gore, J., and Weaver, N. (2021). “On ‘Being First’: The Case for FirstGeneration Status in Australian Higher Education Equity Policy”. The Australian Educational Researcher, pp. 1–19. Peroni, S., and Shotton, D. (2018). “The SPAR Ontologies”. In Vrandečić, D. et al. (eds). The Semantic Web – ISWC 2018. Lecture Notes in Computer Science, vol. 11137. Springer. Reeves, T., Herrington, J., and Oliver, R. (2002). “Authentic Activities and Online Learning”. Proceedings of the 25th Higher Education Research and Development Society of Australasia (HERDSA) Annual Conference, Perth, Western Australia, 7–10 July. Repko, A. F., and Szostak, R. (2020). Interdisciplinary Research: Process and Theory. Sage Publications. Schreibman, S., Siemens, R., and Unsworth, J. (eds.). (2004). A Companion to Digital Humanities. Blackwell. Schreibman, S., Siemens, R., and Unsworth, J. (eds.). (2015). A New Companion to Digital Humanities. John Wiley & Sons. Simpson, R. M. (2020). “Augustine as ‘Naturalist of the Mind’”. In Proceedings of the 31st ACM Conference on Hypertext and Social Media (ACMHT20), Virtual Event, USA, 13–15 July. Snow, C. P. (1961). “The Two Cultures and the Scientific Revolution”. In The Rede Lecture 1959. Cambridge University Press. Svensson, P. (2010). “The Landscape of Digital Humanities”. Digital Humanities Quarterly, 4(1). Wilks, Y., and Brewster, C. (2009). Natural Language Processing as a Foundation of the Semantic Eeb. Now Publishers Inc. Wittgenstein, L. (1922). Tractatus Logico-Philosophicus. London Kegan Paul, Trench, Trubner & Co., Ltd. Harcourt, Brace & Company, Inc. Wood, D., Zaidman, M., Hausenblas, M., and Ruth, L. (2014). Linked Data: Structured Data on the Web. Manning. Zeng, Marcia Lei (2019). “Semantic Enrichment for Enhancing LAM Data and Supporting Digital Humanities. Review Article”. El Profesional de la Información, 28(1), p. e280103.

2

2.1

Privacy, Ethics, and Trust

Preamble

On July 5, 1993, Peter Steiner published a cartoon about Internet anonymity in The New Yorker. The image is well known1: two dogs are at a desk in an office, with a computer on the table. The white dog sits on the floor – the black dog sits on the chair, effectively at the desk, and is turning round to say something to his companion on the floor. The caption reads: “On the Internet, nobody knows you’re a dog”. This now cult status meme seems, perhaps with the clarity brought on by the hindsight of a quarter of a century, to have captured the public’s fear at that time regarding the ability of users to send and receive messages to and from anyone, globally, with no accountability, given that no matter how nefarious the person or their aims, they possessed the ability to hide in anonymity, or the option of using a fake name – the feeling was anyone could (and would!) be out there. Some stranger, someone wholly unknown to you, might be able to communicate with you, or with your children. The danger of the online world, as it was perceived then, was of individual people, bad people, who could find you, but who could not be found in return (by law enforcement, for example). Whether or not this captures a genuine fear, a mere moral panic, or indeed is just a modern reinterpretation with our current fears projected onto it, it is clear the issues of privacy and trust have been a challenge for the users, abusers, and developers of the online medium since the very days of the Web. Those early days in which the Web was launched to the public were in August 1991, two years before this comic was published. Twenty-two years later, on February 23, 2015, and also in The New Yorker, Kaamran Hafeez revisited the motif. This time, both dogs, looking noticeably older, are sitting on the floor with a man sitting at the desk on his machine. The caption this time is “Remember when, on the Internet, nobody knew who you were?”. Fears of the public are no longer of the unidentifiable boogie man lurking among the pixels – the new Big Bad are online oligopolies such as Alphabet2 (the owner of Google, among others) and social media giants such as Facebook, all of them thriving by converting our (often readily disclosed) data into financial gain through targeted marketing and profiling of behaviours. The pendulum has swung to the opposite extreme, from complete anonymity to the death of privacy in this era of the Data Economy (Allen, 2016), where knowledge isn’t just power, it is also cold hard cash. DOI: 10.4324/9781003197898-2

24 Privacy, Ethics, and Trust (Digital) Humanities scholars who investigate the lives of historical persons may not be familiar with the processes of applying for ethical clearance for one’s research, or the issues of privacy when digging into the private lives of their subjects. Computer scientists may be driven by tackling challenges of technical development, or perhaps opportunities for monetisation, and not prioritise concerns for how their tools might be used. Case study examples of machine learning algorithms having gone awry to great public dismay include the Google Photos debacle of 2015 when people were categorised as gorillas;3 when the Technology Review published a story of algorithms showing racist bias when sentencing people to prison;4 and the advertising of chief executive officer (CEO) positions only to men and not women.5 Another example of development and implementation with seemingly little or no consideration for any ethical issues is in the context of Pokémon Go!, an augmented reality (AR) mobile game, where players were lured by the prospect of capturing rare monsters to what were simply inappropriate locations: the National September 11th Memorial, the Arlington National Cemetery, and the Truce Village (in the Demilitarized Zone between South and North Korea), and even Auschwitz (Greenfield, 2017: 64–65). There are, undoubtedly, at least three different considerations at play here: first, whether the tool itself is ethical (or not, such as in the case of the facial recognition app FindFace, which works in conjunction with one of Russia’s largest social media sites, Vkontakte, and “allows users to photograph people in a crowd and work out their identities, with 70% reliability”);6 second, whether the data used to populate the project and train the algorithm is diverse enough (or not, as in the aforementioned case of Google Photos); and finally third, whether users are engaging with tools in an ethical way (Pokemon Go!). In any data collection act, there are also profound ethical considerations as to how the privacy of the individuals whose data is collected can be ensured. 2.2 The Unavoidable Orwellian Reference No paper discussing issues of privacy and surveillance seems to be without its Orwellian analysis. The fruit hangs low for a comparison between Nineteen EightyFour and the state of surveillance technology in the early 21st century. But aligning the two as equivalent or even similar is an oversimplification (Nurmikko-Fuller and Pickering, 2022). Orwell’s vision fails to match the current reality of the Western world in two ways. First, we are not living under the tyranny of a single despotic leader. Second, unlike Winston’s stolen moments with his diary, we have nowhere to hide. As noted by Greenfield (2017), data takes many forms, including that of text and video footage. Social media content is thus a prime example of vast quantities of data produced incessantly by users across the globe. This data which maps our every move, every mood, every meal, everything. The question that may then arise is one of defining what exactly constitutes “surveillance”? To avoid rushing head-first down a rabbit-hole of semantics and dictionary definitions, I’ll cut to the chase: is surveillance which is not just enabled by the huge numbers of pieces of expensive hardware we choose to carry on our

Privacy, Ethics, and Trust 25 person (signs of affluence, social prestige, etc.) or use in our homes but actively feed by the content we deliberately choose to share still surveillance? As we argued in Nurmikko-Fuller and Pickering (2022), social media users are all data producers, but there are myriad other ways we all produce data, even those who abhor the thought of a Facebook profile, those of us who baulk at smartphones, by those who lurk (having created a profile on one platform or another, but do not post content themselves). Each banking transaction, every online bill, those shoes we brought from the Online Only part of the website, the pizza we got delivered yesterday, all those commercial actions leave a digital footprint. So do all your online searches, the shows you stream, the people you message. The epitome of the omniscience and ubiquity of surveillance manifest in products and services (although neither is a product many would wish to acquire nor a service that any of us can easily opt out of) that propose to ease our everyday lives and schedules. Google’s Smart Reply and Smart Compose are very clear examples. As described by Natt Garun in 2020:7 You can also choose to allow Gmail’s machine learning to personalise the suggestions based on the way you write your emails by choosing “Smart Compose personalization”. For example, if you greet your colleagues with “Hi, team” versus “Hello, everyone”, it will automatically drop in whatever you use most often. The crucial phrase here is “you use most often”. The only way this can be achieved is by the systematic and complete reading of all the content you have written to date. Every email, every reply. Where in this scenario is the space to hide from the all-observing screen? There is no nook or cranny of privacy here. “But!” those who would read Garun’s aforementioned article on the Verge beyond that quote will shout “You have to opt into them both!”. And quite right they are. Let’s consider in this context “surveillance” equating to a loss of privacy. Of constant, albeit asynchronous observation. Although the user might not be under scrutiny and recording at all times by Google specially, the total history of collective email correspondence can be examined at any one given time. What can only be said then is that the option of not being observed on the current Web begins to feel like it is simply not an option. Or, perhaps it is an option, but it is not an option that is in any realistic means available to the user, or anyone else who has similar preferences, habits, or lifestyle choices. Profiling is powerful because it does not require the profiler to observe you: it’s enough to observe enough people who are like you. Beyond the overt social media posts of data sharing, we are all observed covertly though data that records, inter alia, medical, financial, and biological data; we are monitored by the state. One of the challenges of Linked Data is actually a privacy debacle waiting to happen: What would happen if all this information from different sources would be deliberately amalgamated to create a comprehensive and complete picture of an individual? Or (more frighteningly perhaps) of a type of individual likely to engage in a particular type of behaviour?

26 Privacy, Ethics, and Trust Given that much of the Data Economy is centred on profiling for behaviours (Bechmann, 2013), that may be all that matters: profit can be turned not just from successful targeted marketing, but also through a process of identifying shared behaviours. User profiling is the method of effectively using the observed and recorded habits of users to predict (rather successfully!) the behaviours of others. Demography, consumer choices, time spent on a page, keyword search patterns, all contribute towards a comprehensive collection of data about a user. In some cases, data aggregated in a specific context has been sold to the highest bidder: one of the more nefarious instances of this being from the UK, where the National Health Service (NHS) sold the data of millions of clients to pharmaceutical companies in 2019.8 In all online activities, there are serious concerns about privacy, ethics, and trust. This is even more poignant when discussing a technology (Linked Data) that is perfectly designed to overcome all aspirations of privacy, and instead combine complementary information about our person (health data, consumer habits, tax records, employment, hobbies, home address, Netflix listings, holiday plans, frequency of ordering pizza, etc.) until a comprehensive and complete picture is formed. Ethical? Of course not. Possible? You bet your cotton socks it is. 2.3

Privacy as a Right, Not a Privilege

In his 2011 TED Talk, Mikko Hyppönen9 described three kinds of online attack. His focus was predominantly on the way governments have used digital technologies such as Trojans to monitor and attack their own citizens. His concluding statement in made in that context specifically: [You might think:] “But why should I worry? I have nothing to hide”. And this is an argument that doesn’t make sense. Privacy is implied. Privacy is not up for discussion. This is not a discussion between privacy against security. It’s a question of freedom against control. And while we might trust our governments right now, right here in 2011, any rights we give away will be given away for good. A decade later, in the aftermath of events like the Trump Administration in the USA, and the Cambridge Analytica scandal, we can see the significance of these words. But, surely democratic governments at least are in some ways regulated, held accountable, controllable in some way? But what of private companies? What about Facebook? The other significant part of the statement is the implication of privacy. Any researcher wanting to conduct experiments or scrape data from Facebook would be faced with a rigorous process of successfully applying for ethics approval. In the case of more open, more public platforms such as Twitter and Reddit, this is relatively straightforward, as the content posted on these forums is open and publicly available. The same cannot be said of all forums and platforms. There is an implied privacy with Facebook, for example, that content is only visible to you

Privacy, Ethics, and Trust 27 and your friends (the specifics have changed over the years as Terms & Conditions have been edited, updated, reworded, etc.). And so now we understand the outrage that led the CEO of Facebook Mark Zuckerberg appearing in front of the American Senate’s Commerce and Judiciary committees10 in 2018 in a hearing addressing Facebook’s privacy policy and their use (and abuse) of data. To quote Chairman Thune: One reason that so many people are worried about this incident is what it says about how Facebook works. The idea that for every person who decided to try an app, information about nearly 300 other people was scraped from your service is, to put it mildly, disturbing. And the fact that those 87 million people may have technically consented to making their data available doesn’t make those people feel any better. The recent revelation that malicious actors were able to utilise Facebook’s default privacy settings to match email addresses and phone numbers found on the so-called Dark Web to public Facebook profiles potentially affecting all Facebook users only adds fuel to the fire. What binds these two incidents is that they don’t appear to be caused by the kind of negligence that allows typical data breaches to happen. Instead they both appear to be the result of people exploiting the very tools that you created to manipulate users’ information. In the aftermath of this intensive scrutiny into the manner in which its users’ data had been insidiously accessed by a third party, Facebook’s numbers and annual turnover declined, but only momentarily: in January 2019, BBC published data showing that not only had Facebook emerged unscathed but its profits had actually increased.11 But at least this furore over Cambridge Analytica – the beneficiary of the raid on Facebook’s data – exposed two things: first, that privacy in the social media sphere was never an option; and second, that confessing to a smaller crime (targeted marketing) appeases many to such an extent that more egregious violations remain undetected let alone punished (Nurmikko-Fuller and Pickering, 2022). The truth emerges that to live in the Data Economy, as we all do, is to live in a time of what Zuboff (2019) called “surveillance capitalism”. Two metaphors of overt and constant observation, Orwell’s Nineteen Eighty-Four (1949) and Jeremy Bentham’s idea of the Panopticon have dominated discourse, but are ill-equipped as metaphors for the types of non-government-driven surveillance that social media giants have so successfully monetised (Browne et al., 2020). The aim of the data collection and user observation is not as a reprimand or punishment, but rather as a method for revenue generation. Surveillance capitalism, which is undoubtedly the true modus operandi of social media giants such as Facebook, is “parasitic and self-referential. It revives Karl Marx’s old image of capitalism as a vampire that feeds on labour, but with an unexpected turn. Instead of labour, surveillance capitalism feeds on every aspect of every human’s experience” (Zuboff, 2019). Since data is the currency of the Age, could data sovereignty and control over our own data present a solution? Certainly, there have been efforts to legislate such power and control over what seems to be as inherently ours as our physical

28 Privacy, Ethics, and Trust bodies – since the law allows us to determine who can have access to by physical being, why would it not extend to the digital representation as well? The policies of the European Union are a good example of exactly that type of policy. Calls for data sovereignty may well have originated in concerns around transnational data flows enabled by cloud computing (Irion, 2012), but it has been increasingly applied to the global movement12 of understanding the term in the context of the data of Indigenous groups and their autonomy from post-colonial states (Kukutai and Taylor, 2016). Alas, no solution has been proven to be a silver bullet. The challenges here are myriad, and range from data literacy to cultural sensitivities; from non-Western knowledge structures to the collection, use, and misuse of Indigenous data; and practical and pragmatic considerations of data acquisition, curation, and maintenance. At the same time, users across all demographic groups have been becoming more aware of the possibility and the need to assert control over their data have had, in some cases, political outcomes. A well-known example of this is the European Union’s General Data Protection Regulation 2016/679 (GDPR). Primarily focused on the regulation in EU law of issues related to data protection and individual privacy (and thus seen, arguably, as predominantly affecting the European Union and the European Economic Area), it nevertheless also addresses the transfer of personal data outside the EU. The concern is that if data could be made into a commercial product, which we could opt to sell, it risks perpetuating privacy as a privilege of the wealthy, who have the luxury of opting not to sell their data. How this would affect the economic models of Instagram influencers or self-perpetuating promotion machines such as the Kardashians (social media socialites, famous for being famous) is unclear. What the immense wealth of the likes of Kim Kardashian (US$1 billion)13 and her sister Kylie Jenner (US$700 million)14 proves is that in the current data climate, there are millions to be made in engaging in the performance of sharing one’s life online. 2.4

Privacy Paradox

No conversation about the publication of data online could be complete without the inclusion of the Privacy Paradox (Gerber N. et al., 2018). And indeed the discussions related to privacy, ethics, and trust in this chapter are in the very context of understanding human behaviour as paradoxical. It is a discussion that appreciates that current social norms dictate that personal data is voluntarily produced and shared as postings of text, image, sound, video, and the combination of all those ingredients. It is an understanding that there are some who have learnt to monetise on the sharing of their data and that these so-called influencers represent a wholly new brand of celebrity. But just as not all musicians become rock stars, so too are the billions of marketing dollars denied to most of the Web’s digital denizens. We are all producing data, but only some have found a way to make the dollars. The Privacy Paradox refers to the “paradoxical dichotomy between privacy attitudes and privacy behaviour” exhibited by users of online (and in particular social media) platforms (Kokolakis, 2017: 125). It stipulates that even when aware of the

Privacy, Ethics, and Trust 29 possibility of surveillance and the compromise of their privacy, users nevertheless choose to engage with social media. As O’Hara and Shadbolt (2014: 5) assert: [the] costs and benefits are the nub of the classic type of privacy problem – there are many tangible benefits to be gained by allowing intrusions into one’s life, but there is also the intangible worry. We simply find it hard, as humans, to balance the tangible benefits and the intangible costs. . . In an evil dictatorship, one has a good idea of how personal information will be used, and so can plan accordingly. But in a capitalist democracy, it is much harder to decide how information will be used in the future. The benefits are there for all to see; the costs are not. This may be why our defences are so often down when our privacy is threatened. There is thus an agreement that users may exhibit, when asked, a pro-privacy attitude, but it is not one strong enough to affect their behaviour. But how much of this narrative makes an implicit assumption that all users are capable of asserting changes to ensure their privacy, and further, that users could, even if they so choose, to opt out? Even in the years preceding the COVID crisis, Zuboff (2019) described the Internet as “essential for social participation”, and suggestions of abstinence from social media were dismissed as detrimental to quality of life and causing of increased loneliness (Vally and D’Souza, 2019). During lockdowns and social distancing, the community-connecting aspects of social media have undoubtedly gone considerably beyond fear of missing out (FOMO). Moore and March (2020) reported on the positive effect of social media as promoting social cohesion, easing the sense of isolation during the pandemic. Wiederhold (2020) and Cauberghe et al. (2021) similarly proved the necessity of social media as a coping mechanism for alleviating anxiety during COVID-19. Barth and de Jong (2017) defined the privacy paradox as the ‘documented fact that users have a tendency towards privacy-compromising behaviour online which eventually results in a dichotomy between privacy attitudes and actual behaviour’ (Barth and de Jong, 2017; see also Acquisti, 2004). In other words, whilst users might be concerned – or express, or feign, concern – about the privacy of their data, the fact is that they are unwilling to change their online behaviour to protect it. The most compelling commodity in this data exchange is convenience, and we sacrifice our privacy on the altar of convenience with relentless enthusiasm. Every social media profile we create; every post we publish; every cookie we accept, every page we cache, as well as every bit and byte of information we insouciantly store in the browser; every automated log of geo-coordinates, provide spatio-temporal information to unseen eyes. We all understand this at some level. But does it affect the way we behave? Abstention from this or that social platform makes no difference. Portable technologies – smart phones, watches, even pacemakers – collect data about us and those with whom we interact as well as those who we simply pass in the street, all without our explicit consent. We are passively, mindlessly, inadvertently the subjects of data collection: our photos are geotagged, our movements are traced via

30 Privacy, Ethics, and Trust phone location, and little additional inference is required to determine where we live, work, or spend our free time (Liccardi et al., 2016). Surfing the net lays bare our consumer choices, hobbies, predilections, and sexual preferences, which can be read in conjunction with State records of our educational attainments, illnesses, bank balances, investments, tax returns, employment history, and even parking fines. New technologies such as AI, deep learning, and machine inference are being applied to extant datasets to predict outcomes as radically diverse as the likelihood of terrorist activity15 and the suitability for a prestigious job or public office.16 Indeed, unsurprisingly, information is systematically collected to identify and then influence our political preferences. 2.5 Trusting Data Producers, Trusting Data Consumers As users of the Web, we have a number of different roles. The three most significant ones are those of the data producer, the data consumer, and information retriever. As data producers, we (as individual users) have long been encouraged to educate ourselves as to the privacy concerns that arise from our willingness to share our lives openly online: Kawase et al. (2013) used the example of critical tweets about one’s employer potentially causing professional damage a decade ago. Arguably, this role is one of privilege and prestige, limited to those who are on the correct side of the Digital Divide: to contribute data to the online world is not cost-neutral, nor is it without its barriers. To post on Instagram, one must have a smartphone or mobile device that in turn needs to incorporate the technology and be in the capable hands of a user with at least adequate skills of photography (not to mention the download and installation of the app itself). We need WiFi or a monthly contract with an Internet service provider (ISP); the hardware has costs, the software has costs, and Web connection has costs. Similarly, the use of the hardware necessitates a degree of skill and manual dexterity, the software requires at least some skill to engage with it; the Internet connection requires the financial means for paying for monthly instalments. However, human individuals are not the only types of data producers: companies, institutions, businesses, conglomerates, and even national governments (in the form of data.gov, data.gov.uk, and others) all produce copious amounts of data, deliberately. Then is also the covertly collected data: the Terms of Service of TikTok, for example, mandate that “you must provide accurate and up-to-date information about yourself (such as your date of birth)”,17 but mention nothing of the “aggressive”18 data harvesting the platform carries out: their Privacy Policy stipulates they collect data about users (name, date of birth, phone number, password, etc.); content (content and its metadata, including geographical locations); purchasing history, and so on. TikTok also collects the content of private messages between users, and, again, the associated metadata. “Even if you are not a user, information about you may appear in content created or published by users on the Platform”.19 Furthermore, as exposed by WIRED in 2021, “TikTok infers factors such as your age range, gender and interests based on the information it has about

Privacy, Ethics, and Trust 31 you. In the US, TikTok can collect biometric information including face and voiceprints”.20 Choice, control, and autonomy have very little to do with how much data is generated, but we are all data producers all the same. Perhaps it is in the category of data consumers that most obviously consist of agents other than individual humans. Depending on where along the workflow we focus, data is retrieved, analysed, and reported on by not only individuals but also research clusters, companies, and even software agents. It is in this space, above all others, where we can see the defining characteristics of the Information Age, the Web sitting at the very core of the Data Economy: knowledge was always power, but now it is also cold hard cash. In this space, we have seen the emergence of commercial clients, of which at the time of writing none could be argued to be more prominent (nor controversial) as Facebook. The most remarkable aspect of this platform is that it is exactly that: a platform, a stage on which we (the users) carry out our performative acts of social engagement. Facebook does not build anything, it does not make anything – all the content, all the reason to be there, as a user, is provided by the user. None can doubt the significance of the Web as the largest, near-ubiquitous hypermedia system that dominates information exchange globally. As information retrievers, we have moved away from trawling through a library’s card index to optimising our keyword searches on Google so that the correct link is displayed above the fold on our screens. Two important considerations arise here: first, the Filter Bubble (coined by Eli Pariser in 2011), a result of Google’s personalised searchers, which prioritises the content we discover online based on prior successful searches (i.e. the links we have clicked on previously), and distorts how we see and understand the world, especially if we are lulled into believing that our search results are unbiased or neutral. Second, is the role that the Web plays as a tool for cognitive extension (Clark and Chalmers, 1998): we might not know all that we know, or be able to remember it, but we know that we can retrieve that information, and what keywords to use to maximise the likelihood of finding our way to that material. To quote Zuboff (2019): “the entangled dilemmas of knowledge, authority, and power are no longer confined to workplaces as they were in the 1980s. Now their roots run deep through the necessities of daily life, mediating nearly every form of social participation” (italics my own, for emphasis). It is thus no hyperbole to say that never before has there been a greater need for digital literacy and critical evaluation of what we encounter online, and a recognition of the agents, which determine the content we see. Finally, in an era of Linked Data and machine learning in a Data Economy, it is not the individual that matters. What is important is behaviour, which can be mapped, modelled, profiled and, ultimately, applied to others. As noted by Zuboff (2019), “surveillance capitalism feeds on every aspect of every human’s experience”. Every human, every experience. Our footsteps count (literally), not us. Profiling provides immense amounts of data that is most valuable when it is aggregate. Even if there is no one else who behaves exactly like you, there are those who behave exactly like you in some ways. When the individual portraits of a cohort are overlaid a digital identikit profile can be formed.

32 Privacy, Ethics, and Trust 2.6

Potential for Disaster

The most prominent selling point of Linked Data – its entire raison d’etre! – is to connect disparate data sources that contain information about the same thing (or the same category of thing) published online. Since this is achieved through the use of HTTP URIs (as described in Chapter 1), each data instance (such as a specific individual, place, or event) and every characteristic about that entity are captured in an entirely unambiguous way. The amalgamation of complementary information from different sources is possible because we can unambiguously state that both datasets contain information about the same thing. For much of research, this is a fantastic thing. Museum collections are enriched by objects and their metadata even if held in collections in different countries (e.g. Europeana);21 numismatists have very successfully merged their data (Nomisma. org);22 sites such as DBpedia,23 Wikimedia Commons,24 and Wikidata25 reach beyond disciplinary silos and merge data covering the contents of the encyclopaedic Wikipedia. At the time of writing, the Linked Open Data Cloud26 had a taxonomy of nine different categories,27 but one of these is the inherently nebulous and undefined “User-Generated” – which, by the way, is something different from “Social Networking”, “Media”, and “Cross Domain”. As of May 2020, the Cloud’s dataset contained 1,301 datasets, which in turn contained 16,283 links.28 These are small numbers: but note that each dataset has a minimum of 1,000 triples and a minimum of 50 links connecting up to other datasets that are already part of the Cloud. A thousand triples may well be the minimum limit for inclusion, but it is by no means representative of a typical dataset. To give some examples, the aforementioned DBpedia’s 2015 release of Data Set 3.8 consisted of 1.89 billion RDF triples.29 The Biblioteca Escolar Digital CITA contains only 930,765 triples;30 GeoNames Semantic Web has 93,896,732 of them;31 the Hellenic Police has a mere 145,368.32 The opportunity and affordance presented by this aggregation of data is the enrichment and diversification of a given topic from a range of sources – and, potentially, the discovery of new information from places and datasets we didn’t know to query. The W3C lists a number of benefits to the adoption of the Linked Data model.33 These range from the somewhat abstract notions of making data “shareable, extensible, and easily re-usable”. What is interesting, telling, and worth noting at this stage is that the W3C has identified different categories of users who they perceive as having a specific and particular interest in the Linked Data methodology. The site lists different categories under four headings: Researchers, Students, and Patrons; Organisations; Librarians, Archivists, and Curators; and Developers and Vendors. The detailed descriptions are themselves rather generic and rather exchangeable between the categories.34 Alas, there is no equivalent page where the W3C would list the drawbacks or challenges of Linked Data. Yet, the Linked Data paradigm is a potential privacy disaster. The practical and pragmatic methods for anonymising data are centred around removal of details. A simple example of this might be a database of addresses. Instead of having the name (say, “Dr Smith”) and the address (e.g. “123 Example Street”) of a person,

Privacy, Ethics, and Trust 33 the database would contain the address, and in lieu of a name, an alphanumeric code to serve as the identifier for the entity dwelling at that address. The pairing for the name and the identifier could reasonably be expected to sit somewhere else, perhaps on an encrypted hard drive, or some other non-Web-enabled device. This seems rather straight-forward. So what’s the problem? The challenge here is Linked Data’s entire purpose is to help us fill in the gaps by bringing in information from other sources. In this case, you might then reasonably expect that there might be another database, which holds, say, the shopping details of Dr Smith. In this database, anonymisation has occurred through the removal of the delivery address of the goods (123 Example Street). So what happens when the two datasets are merged? You got it. Now we know where Dr Smith lives and where they like to shop. This example might seem rather benign, but who among us would like to see different types of information about us combined for use by external entities? Insurance companies, for example, might reasonably be delighted to be able to access information about a client’s lifestyle (from devices and platforms such as Garmin watches, Strava, etc.), shopping habits, and medical records.35 A moral, ethical, and philosophical question might then be: “Should people who have healthier lifestyles be recipients of better/cheaper insurance premiums?”. Or what data is collected, say, by the state, and used to determine whether a person is deserving of benefits, lower rates of taxation, or the ability to vote? It might seem dismissible as dystopian fear mongering, but let’s recall Hyppönen’s warning: access to data once granted to a benevolent state will not be relinquished by a malevolent one. With so much potential for misuse and abuse, why collect this data at all? And why share it? We need to discuss the Quantified Self (QS). 2.7 We Need to Talk About Strava The QS is, in and of itself, a privacy paradox. It is the phenomenon or practice of collecting biological, behavioural, environmental, and other data about oneself. In the mainstream, it is particularly popular with athletes who record data to better understand and develop their training. Information about heart rate, speed, cadence, global positioning system (GPS), and a myriad other details are diligently collected. All this information is useful, valuable, and for athletes, trainers, and indeed anyone who fits the label of a “data fetishist” (Sharon and Zandbergen, 2017), genuinely interesting. But what could possibly go wrong with sharing this data openly? Let’s focus on an easy example: location data. In the days before Uber, few of us would have considered giving our address to a complete stranger online. Because they might find you! But now, we give this information to strangers, Whose very purpose, and (very literal, in the case of Uber!) drive is to come and get you! The obvious workaround for stationary location data is to move to a random or meaningless location, but this is not always possible: swimming pools, for example, are rather immobile, running tracks also, and even cycling routes and lanes are rather immutable. Add to this the inherently repetitive nature of training in terms of location, time of day, and frequency, and the data could be easily used to predict the

34 Privacy, Ethics, and Trust next time a user will be at a specific location. Data from Strava (an American Web platform that enables users to track physical exercise such as running and cycling and to share that data with others through the platform’s social networking functionality) has already been used to identify hidden locations using this pattern of training (in this case, a military base).36 But the scary thing of location tracking data goes further still. In their 2016 paper, Liccardi et al. proved that simply by observing location and time of day, participants were able to determine whether the data in question displayed the subject’s home address, work place, or route on their commute. Of these, the commute was the easiest to spot, and the place of employment the second. Simply by combining two types of information (when and where), it would be possible to infer greater detail about a person. Where do they work? What do they do? A location pin at a military base or a university campus might be a clue. It is not just a matter of being vulnerable when you are found: the absence of the resident from a home would naturally make it more attractive to opportunistic crime. Interestingly, much of the academic work using Strava data is firmly in the space of improving and developing high-performance cyclists (Lee and Sener published a literary review on the topic in 2021), or in studies examining the effect of air pollution (Sun et al., 2017). Popular media on the other hand is a rich source of content about undesirable behaviours enabled by platforms like Strava, such as “Strava-stalking”. Elizabeth Barber (writing for WIRED, a magazine focusing on technology, society, and politics)37 and Katie O’Malley (for ELLE, a lifestyle magazine that mostly focuses on fashion)38 have both published in a confessional sense (albeit in styles of writing that flirt dangerously with trivialising the problem). Others seem to consider it perfectly reasonable to look up the details of a person who shares a short segment of tarmac with them. One user of an open but specialist forum commented “I just looked at who was in front of me on a local segment, and googled a few of them. One guy keeps cropping up so I posted a comment on one of his rides which is pretty close to one of mine”.39 Given then that a person could leave themselves so vulnerable by sharing location, surely no one would opt to do that? That is precisely where the paradox kicks in. Especially with many athletes, the QS provides an additional inspiring opportunity for competition. Who wants to win on just race day, when you can win the daily race? In January 2022, Strava had 76 million users.40 And that number is increasing. But whose responsibility is it when things go wrong? There is a further twist to the tale of mixing QS data with third-party software and services. Via Strava, data isn’t just shared between people (other users), it can also be shared across different hardware, software, and companies. Those familiar with the Facebook and Cambridge Analytica scandal in the 2010s41 will know that data sharing (or selling) between companies can elicit strong reactions – in the aftermath of public outrage, lawsuits, a Netflix documentary, and fines to the British Information Commissioner’s Office, Cambridge Analytica filed for bankruptcy. Facebook emerged largely unaffected: in the months following the scandal, there was a reported drop in posts,42 but, online movements such as #DeleteFacebook have had no effect: Statista reports a total of 2.91 billion monthly users for the

Privacy, Ethics, and Trust 35 social media behemoth’s site.43 Alternatives such as WhatsApp and Instagram may have attracted more users as a result – this is a rather moot point from a data privacy perspective since both platforms are owned by Facebook. The issue here is largely reported to be a discrepancy between what data Facebook and Cambridge Analytica were claiming to be sharing, and what they actually did. The numbers were huge: 270,000 Facebook users opted in. But as large as these numbers were, they were not the full story: it was not only these users but their entire social networks that were collected, resulting in a final figure of 87 million profiles being affected.44 Public outrage (albeit one that was not widely published) ensued when Strava disconnected its ties with Apple Health in March 2022. Prior to this date, Strava users could and would use the service to collect data about their athletic achievements. Strava shares this information from and with other third parties. For years,45 users could sync up their data from various different pieces of equipment to Strava (including watches like Garmin, Polar, etc.), and through to Apple Health. Successful completion of the tasks would result in a satisfying data visualisation on the watchface referred to as “Closing Your Rings”.46 Without much notice, Strava opted to disable the backend functionality that made that sharing with Apple Health possible. DC Rainmaker critiqued the decision, saying: There’s not a single sport/fitness device I’m aware of that doesn’t integrate with Strava. Thus, it’s become the de facto interchange for sport/fitness data over the last few years. And while Strava undoubtedly touts that often, I don’t think they’ve fully grasped the downstream ramifications of that. Either in policy or technically . . . Strava’s solution is for you to spend less time with their platform. I can’t imagine any company saying “Look, how about you don’t use our services?” . . . they don’t understand the technical ramifications of why their workarounds don’t actually work. Never mind that’s not a consumer-friendly practice anyway.47 So strong was the public backlash, so unwavering the demand to have their data shared across platforms, that Strava backtracked on the decision, and re-established this possible syncing.48 But why does any of this matter? Because it illustrates the disjoint between assumptions that the privacy paradox is the exclusive right of Instagram influencers or teen agers struggling with a sense of potential social isolation. The privacy paradox is much more an issue of convenience/entitlement to service that it is of FOMO. And, to refer back to the earlier question about surveillance, is it surveillance still if the user demands to have the right to have their data issued to several different companies, any of which might utilise it for analysis, target marketing, user profiling, or monetise it the simpler of way of just sell it on to someone else? At what point of the data consumer to surveillance victim pathway does the responsibility of the user over their own data come into play? And, if we know this is happening at an industry level, and enabled by companies and users alike, why do we get some antsy at the prospect of this data aggregation

36 Privacy, Ethics, and Trust happening through software or methodologies like Linked Data? Is there any difference if we are surveilled by people or by algorithms? 2.8

On the Questions of Ethics

The topic of ethics is a huge and complex space. Even if focusing exclusively on the role of ethics in context to research carried out in the Digital Humanities, the topic is multifaceted and complex. As discussed earlier, one of the major considerations of using Linked Data in particular is the potential violation of privacy it presents: this is a considerable ethical consideration. In 2022, the 20th European Networked Knowledge Organization Systems (NKOS) Workshop was carried out (virtually) as part of the Joint Conference for Digital Libraries.49 It centred on issues relating to topics of multilingual, multicultural, unbiased knowledge representation with considerations of diversity, equity, and inclusion. One of the prevailing challenges is around inclusion of diversity within research and the inclusion of different perspectives into knowledge representation paradigms: questions around terminology and semantic shifts (e.g. should “illegal aliens” be substituted with “undocumented immigrants”?). The Getty Vocabularies have opted for an approach of assisted decision making, and the provision of contextual information: For example, terms that have been deemed pejorative or objectionable are labelled “avoid use” for new indexing, but historical, obsolete, or objectionable terms are not deleted since they provide access and enable discovery.50 The use of the Homosaurus vocabulary (discussed further in Chapter 4) in Sweden51 and the representation of Indigenous knowledge52,53,54 flag the need for considerations such as the need to be sensitive not only to the desires and rights of present-day people (e.g. when describing the gender or identity of an artist) but also in applying that sensitivity retrospectively to historical data, such as the preferred name of places, locations, and sites. Metadata records of various cultural heritage collections across the globe will need to begin to consider how they will update terminologies without affecting indexing and thus information retrieval. Existing guidance for research data particularly in relation to Indigenous material exists in the form of the CARE data principles (discussed further in Chapter 3).55 In the Australian context, the National Statement on Ethical Conduct in Human Research56 is almost entirely silent on the topic of collecting and analysing digital data: in fact, the word “digital” occurs only once in the documentation (p. 33), but it does refer to “social media” more frequently (five times in total). These ethical considerations are related to “recruitment strategies [which must] adhere to the ethical principles of justice and respect” (p. 28), privacy concerns (p. 36) and the Terms and Conditions of various different platforms (p. 37). The topics on diversity and inclusions were flagged by Estill et al. (2022) in the context of the Digital Humanities and the disciplines annual international flagship conference as well. Issues of (prohibitively) high costs of attendance, lack of linguistic diversity, and the ethical division of labour in conference organisation highlight that not only is the research process itself that is worthy and needing of ethical

Privacy, Ethics, and Trust 37 considerations and sensitive approaches, but also, indeed the entire academic landscape, from institutional hiring practices to the dissemination of research outputs, which need to be examined and evaluated for thoughtful, equitable, and ethical research. 2.9

Conclusion

This chapter has discussed how the fundamental concepts of privacy, ethics, and trust sit at the core of the Linked Data paradigm. Thought-experiments and real-life examples have illustrated how disambiguation and amalgamation of information from several different sources fill in the gaps to thwart any and all attempts of anonymisation (privacy); to question where we should draw the lines regarding what data should be Open and Linked (ethics); and highlight the several different ways in which trust plays a fundamental role in knowledge exchange. Is privacy a fundamental right that should be protected, a privilege of the affluent, or indeed something already irretrievably lost? The notion of privacy is neither straightforward to define nor a universal constant. The discussion in this chapter is situated within a historical context, examining the development of the concept of individual privacy and one’s right to it as seen and experienced through a Eurocentric lens. The second section of the chapter focused on the question of ethics especially when using the Linked Data method with personal, financial, medical, socioculturally sensitive, or otherwise private data. It also questioned who should have access to the merged data, and whether or not authorities and governments should be enabled to create comprehensive pictures of individuals. Trust is given and earned by data consumers and data producers. It is worth questioning the effect living in the Data Economy has had on our approaches to information exchange. The social media revolution saw us all turn into data producers, and fundamentally changed the information publication paradigm. A decade or so later, we live in a time of fake news and alternative facts. Has the democratisation of information production caused a devaluation of information authorities and official sources, and how this phenomenon might affect the Linked Data paradigm? Should all relevant datasets be connected to? If not, who gets to decide what is included, and what is not? The topics of privacy, trust, and ethics are all immense, and any one of them could easily warrant an entire career examining them. To merge them all into one chapter has done little but set the scene for a more detailed discussion of the Linked Data paradigm, but one that is grounded firmly in the understanding of these topics, and an awareness of the potential pitfalls. Our ideas about privacy, ethics, and trust are multifaceted, but they have also changed – from the perspective of society, expectation, cultural norms, and so on – over time. The issues of data sovereignty and user responsibility are recurring motifs in the conversations regarding these topics, but until there are clear financial or legislative incentives or rules in place, platforms are unlikely to make fundamental changes to their modus operandi. As with any system of supply and demand, the

38

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Data Economy is unlikely to disappear until there is some grand change to the way we produce and consume content online. The question of whether it may or may not be ethical to merge readily available data may fall silent when faced with a public reaction that is rather reminiscent of the Three Wise Monkeys: it is completely OK for platforms to make millionaires out of their shareholders using data about us, as long as there is a societal agreement to turn a blind eye to it all. But hey, who doesn’t want a nifty tricoloured circle on their watch once a day? Notes 1 It is readily available online (indeed, it has its own Wikipedia page), but copyright restrictions mean that inclusion of the image in this book would either incur a financial cost, or constitute copywrong (the unethical or illegal use of material through disregard for copyright law). 2 https://abc.xyz/. Accessed 11/06/2021. 3 www.theverge.com/2018/1/12/16882408/google-racist-gorillas-photo-recognitionalgorithm-ai. Accessed 27/05/2022. 4 www.technologyreview.com/2019/01/21/137783/algorithms-criminal-justice-ai/. Accessed 25/02/2021. 5 www.independent.co.uk/life-style/gadgets-and-tech/news/google-s-algorithm-showsprestigious-job-ads-men-not-women-10372166.html. Accessed 25/02/2021. 6 https://www.theguardian.com/technology/2016/may/17/findface-face-recognition-append-public-anonymity-vkontakte#:~:text=FindFace%2C%20launched%20two%20 months%20ago,identities%2C%20with%2070%25%20reliability. 7 www.theverge.com/21315189/gmail-ai-smart-reply-compose-tools-enable-turn-onhow-to. Accessed 04/04/2022. 8 www.theguardian.com/politics/2019/dec/07/nhs-medical-data-sales-american-pharmalack-transparency. Accessed 05/04/2022. 9 www.ted.com/talks/mikko_hypponen_three_types_of_online_attack. Accessed 25/02/2021. 10 www.washingtonpost.com/news/the-switch/wp/2018/04/10/transcript-of-markzuckerbergs-senate-hearing/. Accessed 25/02/2021. 11 www.bbc.com/news/technology-46755608. Accessed 27/05/2022. 12 https://aiatsis.gov.au/publication/116530. Accessed 19/05/2022. 13 www.forbes.com/sites/maddieberg/2021/04/06/kim-kardashian-west-is-officially-abillionaire/?sh=1bef518121bb. Accessed 25/02/2021 14 www.forbes.com/profile/kylie-jenner/?sh=3a70b59155b5. Accessed 25/02/2021 15 www.technologyreview.com/2019/01/21/137783/algorithms-criminal-justice-ai/. Accessed 25/02/2021. 16 www.independent.co.uk/life-style/gadgets-and-tech/news/google-s-algorithm-showsprestigious-job-ads-men-not-women-10372166.html. Accessed 25/02/2021. 17 www.tiktok.com/legal/page/eea/terms-of-service/en. Accessed 30/01/2023. 18 www.theguardian.com/technology/2022/jul/19/tiktok-has-been-accused-of-aggressivedata-harvesting-is-your-information-at-risk. Accessed 30/01/2023. 19 www.tiktok.com/legal/page/eea/privacy-policy/en. Accessed 30/01/2023. 20 www.wired.co.uk/article/tiktok-data-privacy. Accessed 30/01/2023. 21 https://pro.europeana.eu/page/linked-open-data. Accessed 25/02/2021. 22 https://nomisma.org/. Accessed 05/04/2022. 23 www.dbpedia.org/. Accessed 05/04/2022. 24 https://commons.wikimedia.org/wiki/Main_Page. Accessed 05/04/2022. 25 www.wikidata.org/wiki/Wikidata:Main_Page. Accessed 05/04/2022. 26 https://lod-cloud.net/. Accessed 05/04/2022.

Privacy, Ethics, and Trust 39 27 These categories are: Cross Domain, Geography, Government, Life Sciences, Linguistics, Media, Publications, Social Networking, and User-Generated. 28 https://lod-cloud.net/#about. Accessed 05/04/2022. 29 http://downloads.dbpedia.org/wiki-archive/data-set-38.html. Accessed 05/04/2022. 30 https://lod-cloud.net/dataset/biblioteca-escolar-digital-cita. Accessed 05/04/2022. 31 https://lod-cloud.net/dataset/geonames-semantic-web. Accessed 05/04/2022. 32 https://lod-cloud.net/dataset/hellenic-police. Accessed 05/04/2022. 33 www.w3.org/2005/Incubator/lld/wiki/Benefits. Accessed 05/04/2022. 34 Why, for example, would creating “an open, global pool of shared data that can be used and re-used to describe resources, with a limited amount of redundant effort” be exclusively of interest to libraries? 35 https://slate.com/technology/2014/09/insurance-companies-are-using-quantified-selfdata-for-accountability-tracking.html. Accessed 06/04/2022. 36 www.bbc.com/news/technology-42853072#:~:text=Online%20fitness%20tracker%20 Strava%20has,the%20heatmap%2C%20a%20spokesman%20said. Accessed 06/04/2022. 37 www.wired.com/story/strava-love-surveillance/. Accessed 07/04/2022. 38 www.elle.com/uk/life-and-culture/culture/a32955987/strava-stalker/. Accessed 07/04/2022. 39 https://singletrackworld.com/forum/topic/strava-stalking-etiquette/. Accessed 07/04/2022. 40 www.businessofapps.com/data/strava-statistics/#:~:text=Strava%20currently%20has%20 76%20million,adds%20one%20million%20every%20month. Accessed 06/04/2022. 41 An event of such historical magnitude that it warranted a Wikipedia entry: https:// en.wikipedia.org/wiki/Facebook%E2%80%93Cambridge_Analytica_data_scandal. Accessed 07/04/2022. 42 www.theguardian.com/technology/2019/jun/20/facebook-usage-collapsed-since-scandaldata-shows#:~:text=Since%20April%202018%2C%20the%20first,the%20business% 20analytics%20firm%20Mixpanel. Accessed 07/04/2022. 43 www.statista.com/statistics/264810/number-of-monthly-active-facebook-usersworldwide/. Accessed 07/04/2022. 44 www.theguardian.com/technology/2019/mar/17/the-cambridge-analytica-scandalchanged-the-world-but-it-didnt-change-facebook. Accessed 07/04/2022. 45 www.dcrainmaker.com/2022/03/strava-abruptly-ends-3rd-party-data-push-to-applehealth.html Accessed 06/04/2022. 46 www.apple.com/au/watch/close-your-rings/. Accessed 06/04/2022. 47 www.dcrainmaker.com/2022/03/strava-abruptly-ends-3rd-party-data-push-to-applehealth.html. Accessed 07/04/2022. 48 www.tomsguide.com/news/strava-just-removed-this-key-apple-watch-feature-but-wehave-a-solution Accessed 07/04/2022. 49 https://nkos-eu.github.io/2022/programme.html. Accessed 26/01/2023. 50 https://nkos-eu.github.io/2022/content/NKOS2022-presentation-harpring.pdf. Accessed 26/01/2023. 51 https://nkos-eu.github.io/2022/content/NKOS2022-abstract-golub.pdf. Accessed 26/01/2023. 52 https://nkos-eu.github.io/2022/content/NKOS2022-abstract-shiri.pdf. Accessed 26/01/2023. 53 https://digital.lib.washington.edu/researchworks/handle/1773/46601. Accessed 26/1/2023. 54 https://nkos-eu.github.io/2022/content/NKOS2022-abstract-kong.pdf. Accessed 26/01/2023. 55 www.gida-global.org/care. Accessed 25/02/2021. 56 www.nhmrc.gov.au/about-us/publications/national-statement-ethical-conduct-humanresearch-2007-updated-2018. Accessed 26/01/2023.

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3

3.1

Closed But Not for Business

Preamble

Do you remember when Lars Ulrich, the drummer and founding member of the American heavy metal band, Metallica, testified before the U.S. Senate Committee on the Judiciary in relation to legal action the band was taking against an Internetbased company? Ulrich told the members of the Committee that the band had been alerted to the fact that their music was being “hijacked”. Napster, he stated, had enabled anyone “around the world” to illegally download their entire copy-righted music catalogue in a digital format from the Internet for free. Napster, he continued, had “never invested a penny in Metallica’s music or had anything to do with its creation”. The band’s legal investigators had identified 335,435 users of Napster’s peer-to-peer platform who had downloaded Metallica’s music. Ulirich likened them to those who had “won one of those contests where you get turned loose in a store for five minutes and get to keep everything you can load into your shopping cart”. “With Napster, though”, he testified, “there’s no time limit and everyone’s a winner – except the artist”. Ulrich’s testimony, and Metallica’s action more generally, attracted a flurry of attention, including the master satirists, Trey Parker and Matt Stone, who included it in an episode of South Park that aired in 2003: Kyle: We d-didn’t think it was that big a deal. Detective: Not a big deal! You think downloading music for free is not a big deal?! Put your coats on! I’m gonna show you something! And I don’t think you’re gonna like it! This is the home of Lars Ulrich, the drummer for Metallica. Look. There’s Lars now, sitting by his pool. Kyle: What’s the matter with him? Detective: This month he was hoping to have a gold-plated shark tank bar installed right next to the pool, but thanks to people downloading his music for free, he must now wait a few months before he can afford it.1 The spirited rebuttal before the Committee by Napster’s CEO, Hank Barry, is less well known. Napster was not infringing copyright law, he told the Committee, and the hostility to his company merely reflected the music business’s DOI: 10.4324/9781003197898-3

Closed But Not for Business 43 failure to come to terms with new technology. Metallica had sued Napster for $10 million and effectively won the case when it was settled out of court. By this time, Metallica had been followed into Court by other artists seeking recompense from Napster for breach of copyright. Napster had been launched in 1991, and by 2001, it was facilitating tens of millions of music transfers per month. By the time Metallica and others had finished pursuing it, the corporation had filed for bankruptcy. It proved to be a pyrrhic victory. Napster was followed by a raft of other peer-to-peer sites, each shut down in succession, but even at the time of writing, Metallica’s catalogue can be found on-line and down-loaded for “free”.2 Today massive corporations, most notably the Swedish subscription platform, Spotify, have transformed the legal consumption of music on-line into a multibillion dollar business and the only squabbles are among each other. And, it’s all just data anyway isn’t it? Well it is, and it isn’t. Or, at least it’s not that simple. Metallica v. Napster highlights complexity. What began as composed music was rehearsed, performed, recorded onto analogue tape or digitally, pressed onto vinyl and produced as CDs, commodified (sold), broadcast (again sold), ‘hijacked’ and given away for “free” (actually monetised again), downloaded and at various points in this process, it became music again. In other words, data is complex: data is everywhere, and everything is data. In general discourse, especially in the Digital Humanities, we may often cognitively shortcut to thinking about data as just digital data (spreadsheets are often what springs to mind), but the Humanities are rich with structured, semi-structured, and even unstructured data: every collected dataset, every museum database, every narrative, every interpretation of historical facts, a melody of a composition, the subject of a painting. Data is ubiquitous. 3.2

Data

Data as a concept is something that needs to be understood and appreciated for its complexity and nuance. Living as we do in the Digital Age, data is often a shorthand term for digital, quantitative data. But that’s not all the data there is. Material collected from interviews is a prime example of qualitative data; research in the Humanities is (and has been for centuries) focused on the use and close study of unstructured data. And this is also true of the Digital Humanities, which has a multitude of data, both qualitative and quantitative, both digital and analogue. The reference model for Open Archival Information Systems (p. 10 of Section 1)3 defines data as “interpretable representation of information” and provides examples such as tables with numbers, text, audio recordings, material objects, and digital data (at the level of bits); the National Statement on Ethical Conduct in Human Research4 categorises data (including summary data and metadata) into raw, cleaned, or transformed; it cites, inter alia, examples such as interviews, questionnaires/ surveys, personal histories and biographies, images, audio recordings and other audio-visual materials, observations, administrative records, and digital information about mobile device usage as data.

44 Closed But Not for Business We can add to this historical and cultural heritage data, and indeed just about any written materials, depictions, sounds, or any other materials that tell us something of the world. Primary sources especially in the Humanities can easily constitute materials which are not digitised and which cannot be accessed using web crawling or scraping: the analogue hypertext system reported on in Nurmikko-Fuller and Pickering (2021) is a prime example of this. The Digging into Data Challenge5 has funded 50 projects focusing on myriad different kinds of data, much of which is not digital. Data is attached to a number of adjectives: data can be Big, Open, Linked or Meta. It can be machine- or user-generated, it can be real-time. Many verbs lend themselves happily to being the suffix to the data-prefix: engineering, governance, mining, production, consumption, storytelling, literacy, aggregation, modelling, clearing, and visualisation. You can even combine it with nouns: fabric, lakes, analyst, architecture, model, warehouse, and culture. And to this we can add one more in the form of “divided data” (a term coined by one of my anonymous reviewers) – to refer to collections of information such as spreadsheets and lists and databases, but ones that have not been connected to any other information sources with the use of Linked Data or any other aggregation process. These datasets are the ones that are likely to correspond to suboptimal research data management plans: yes, they sit on the hard drives of specific individuals. But how can these datasets be recorded, discovered, utilised? The sheer volume of data created and generated but potentially lost is bound to be staggering. Perhaps the Humanities could consider a paradigm shift, where the data, as well as its interpretation, would be recognised as a valid research output? But not all data is generated as a result of robust academic endeavour. The most obvious context in which we can see user-led data production in overdrive is social media. The multimedia giants that dominate the online world are, at the time of writing, Facebook, Instagram, and (Elon Musk’s acquisition of the platform notwithstanding) Twitter, but also include discussion forums and text-heavy platforms such as Reddit, as well as the more niche environments of LinkedIn, ResearchGate, and others. Each posting of content, be it a peerreviewed journal article, or even a TikTok video that fails to go viral, is our contribution to the abundance of data available online. This is not a new problem: this constant production of new content has been described using terms such as “an avalanche”, and “a deluge” for a decade if not two. The choice of metaphor is not accidental: both terms convey the notion of the sheer overwhelming quantity of material. The subdomains of HASS are an incredibly diverse field with a staggering range of domains, methodologies, heuristics, epistemologies, and research agendas. HASS encompasses fields as diverse as history, music, library and information sciences, archaeology, social sciences, art, and anthropology, but to name but a simple few. It is home to a range of methodologies, from qualitative interviews and the collection of oral histories, to quantitative analyses of data collected from online posting and social media. Yet even with all this internal diversity, there is something that is unique that combines the subdisciplines of HASS. But what is

Closed But Not for Business 45 this unifying feature? Certainly, it is the focus on human endeavour, and on how humans have experienced, interpreted, and interacted with the world around them. It may not be a truth universally acknowledged, but the Humanities has data. Without a doubt. It’s there, everywhere. But what makes it tricky is that most Humanities data simultaneously has one or more of four defining characteristics: it is ambiguous, messy, incomplete, and unstructured. The fifth element is one of (un) reproducibility. 3.2.1 Unreproducible Data

Humanities data is in many ways unreproducible. The statement might initially sound absurd, but on closer inspection can be shown to ring true. The secret is in the explicit articulation and definition of what we mean when we say “Humanities data”. As a premise, let’s agree that Humanities data can be one of two things (or indeed both). It can be the primary source (an original manuscript, or a painting, say), or it can be the result of a process of analysis, a secondary source that amalgamates a number of primary sources and uses them as evidence to support a conclusion. In some ways, we could understand these two datasets as being part of one biography of an information object: the primary sources are a necessary part of the life span of the thing of which the secondary sources are a final or subsequent result. Perhaps we can equate this idea to the life of a butterfly: the primary sources are the caterpillar, the secondary sources are the butterfly. The pupa is a black box, a process during which transformation occurs. But how does this explain the (un)reproducibility of Humanities data? Undoubtedly, the secondary sources are at least to some extent reproducible. We present the information (all the things we know about our caterpillars!) and use it to justify the ultimate butterfly. Others observing our caterpillars and the metadata of our caterpillars will more or less be able to, if not agree with the final butterfly, then at least see why this is the butterfly we produced. But what all Humanities primary data has in common is a degree of undefinability. The primary source cannot be reproduced, because the primary, original datasource (the manuscript, the musical score, the painting) is a manifestation of an abstract notion that can only exist in the mind of the creator. We can thus never start from the start. This notion has been explicitly captured in the CIDOC CRM6 and FRBRoo7 ontologies (discussed in greater details in Chapter 4) that capture cultural heritage data (museums and libraries respectively). The latter for example explicitly differentiates between E28 Conceptual Object (which “comprises nonmaterial products of our minds . . . [that] cannot be destroyed. They exist as long as they can be found on at least one carrier or in at least one human memory. Their existence ends when the last carrier and the last memory are lost”),8 and E73 Information Object (which “comprises [of] identifiable immaterial items, such as poems, jokes, data sets, images, texts, multimedia objects, procedural prescriptions, computer program code, algorithm or mathematical formulae, that have an objectively recognizable structure and are documented as single units”),9 and E33

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Linguistic Object (“[including] written texts, recorded speech or sign language”),10 although the ontological structure representing an idea, the conception and expression of that idea, the documents carrying the information of the idea, and the content of the inscription in which the idea is formulated is far more complex in the CIDOC CRM than these three Classes alone. Perhaps then it is simply impossible to start at the very start of the biography of Humanities data. We do not possess the means of recreating the cognitive processes that existed in the past, or ephemeral performances that disappeared as soon as they were no longer being observed and experienced. Undoubtedly, for the Humanities researcher, what can only ever exist by its very definition is a manifestation of a cognitive process (itself powered by innumerable tacit and intangible aspects of the lived experience, feelings, idiosyncratic aims and intentions of the creator) – and itself is already the result of an act of interpretation, a product of an act of translation of an intangible, nebulous, and abstract concept into a thing that enables the communication of that idea to someone else. What we can analyse is the record that captures the process, the event, the moment, but we never have the equivalent of what might constitute “raw” data. 3.2.2 Unstructured Data

Data is everywhere, and everything. As Greenfield (2017: 211) puts it: Most of the world’s data . . . does not happen to reside in the neat tablet or crispy cellular structure of any database. . . So the new ways of handling such situations is to look for emergent patterns in previously unstructured data, like a large body of text, a series of images, or indeed a real-time video feed. But how easy is it for us to forget that all this content equates to all this data? Schöch (2013) somewhat anecdotally noted that the vernacular of scholars in the Humanities has not traditionally described the analysis as focusing on “data”. The material is described more in terms of the objects that constitute the primary source(s) – newspapers, books, plays, musical compositions, paintings, and so on. Neither these objects nor the historical context that provides much of the metadata about them (who made them, who owned them, what do they capture, how are they to be understood, what role did they play in contemporary society, etc.) are necessarily or frequently represented in structured formats such as tabular data held in a spreadsheet. Yet, there is still much information here, a lot of data for analysis. Details of the object biography can be, and are, collected and stored exactly in such a way by memory institutions (museums, archives, libraries), but for many Humanities and Digital Humanities scholars and researchers, pulling out significant details from unstructured data (say, for example, people or places embedded in the narrative of a piece of writing) to create datasets forms a significant if somewhat thankless part of the workflow. Table 3.1 illustrates how the unstructured text of a piece of fictional narrative could be turned into structured, tabular data.

Closed But Not for Business 47 Table 3.1 From unstructured narrative to tabular: a location-based example Unstructured data

Location data

“The Park Lane Hospital for the Dying was a sixty-story tower of primrose tiles. As the Savage stepped out of his taxicopter a convoy of gaily-coloured aerial hearses rose whirling from the roof and darted away across the Park, westwards, bound for the Slough Crematorium” (Huxley, 1932: 163).

Park Lane Park Lane Hospital for the Dying the Park Slough Crematorium

Whilst named entity recognition and disambiguation remain some of the challenges of natural language processing, their importance as tools for working with descriptive and unstructured data cannot be questioned (van Hooland and Verborgh, 2014: 168–169). But in some aspects, pulling the tangible, structured data out of text is itself an act of data management, curation, and thus, ultimately, of interpretation. In many ways, the example in Table 3.1 is less indicative of purely unstructured data, which might be better characterised with an example of the plot of a novel. A commonly occurring subset of data, particularly in the GLAM sector, is one of semi-structured data, typically exemplified by those unstructured sections within metadata records. Anyone with familiarity with a collections management system will know of an equivalent of an information category referred to as “notes” or “misc.” or “miscellaneous”. Zeng (2019) provides examples of structured data (catalogues, archival finding aids, indexing and abstracting databases, curated research datasets, etc.), semi-structured data (e.g. unstructured sections within otherwise structured datasets or within metadata; TEI files’ content outside of the Header section), and unstructured data (documents, cultural artefacts, etc., which can be either digitised or analogue; covering all kinds of media and formats), which illustrate this point perfectly. 3.2.3 Ambiguous Data

Case study examples of ambiguity in Humanities data are not difficult to find. The historical use of patronyms and the tradition of apprenticeships within families makes the following scenario familiar to most prosopographers. Imagine a set of census data. In the list, there is an address for the people who live there, and their prospective employment status. How then could we differentiate, for example, a father and a son, who live in the same address, and who work in the family business? A dataset recorded as a snapshot in time would not necessarily include any additional information, and disambiguation is only possible through the addition of information from external sources. There are other sorts of ambiguities in data. These could be problems of disambiguation as above, or problems with data alignment arising from issues such as the

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omission of part of a word. Let’s consider the case of two names: Jane Smith and Jane A. Smith. A character recognition process might fail to identify them as referring to the same entity, because the letters in the strictest sense do not adhere to the same identical pattern. To human cognition, especially where aided by additional knowledge about the person (e.g. that the person’s middle name is Agnes), there’s little ambiguity here, merely more information in one context than the other. Such discrepancies in the data can cause challenges for data scientists or anyone wishing to aggregate data from these two contexts. Without foreshadowing the remainder of the book too much, this is exactly the sort of problem that Linked Data could help us solve. 3.2.4 Incomplete Data

Humanities data is incomplete. This axiom of information completeness becomes ever clearer and more truthful the further back in time we move. The evidence we have from the ancient world, for example, is eloquently defined by Cunningham (2013: 99) as being “highly erratic . . . partially due to accidents of modern recovery and partially to accidents of ancient survival”. These twin considerations of Preservation and Discovery reflect the truth that not all instances of all creative and intellectual human endeavour have survived, nor has all that has survived been rediscovered to date. It would be one thing to say that Humanities data is incomplete, and that interdisciplinary researchers and domain experts in various fields within HASS must accept their fate as working with datasets which Taylor (2013: 290) so poetically equates to the night sky, with information dotted throughout the vastness of the unknown as “scattered specks of light against an overwhelming sea of darkness, punctuated by a few spots of intense brightness”. The truth alas is a degree more complex, for new information in the form of long-lost or recently discovered primary sources emerge through scholarship in archives, museums, and collections of all kinds. To extend Taylor’s metaphor, our knowledge of history may well be like the night sky, but we are forever discovering new stars in an infinitely expanding universe, and have only begun to explore the role of the dark matter and dark energy that fills what we thought was the empty space in between them. A consideration which will undoubtedly seem obvious to some, and shocking to others is simply this: put, there is no incentive, desire, or reason for Humanities scholars to remove the complexity nor ambiguity from their data – no aim to fill in the gaps or tidy up the datasets. Consider for example a scenario of a historical document. The first page of the document describes some of its metadata, namely the publication date, with the following words: “The yere of our lorde a. M.CCCCC. and .ix.”. This might present a challenge for someone tasked with entering this information to a collections database. First, because it is not just a string of letters but indeed a cluster of words for a piece of data for which one might rightly expect a number; second, because it is not fully written in according to the rules of contemporary English, but instead represented with Roman numerals (thus requiring some domain knowledge); and thirdly, it might not have the granularity necessitated by

Closed But Not for Business 49 the database schema, which might adhere to the DD-MM-YYYY format. What should the person responsible for the data entry do? For a historian, the answer is to do nothing at all. The absence of a data unit would itself be informative about the document, and the insertion of false data (inaccurate date) would be an unfathomable abomination. The absence of data is by far a lesser crime than the introduction of false positives. But what of databases that force the user to enter a date? For any data and computer scientist, the answer is likely to be January 1st. But why? One is undoubtedly from familiarity, and perhaps a belief that the date is innocuous: 1st January 1753 (1753–01–01) is the minimum date value for the Datetime field in SQL Servers, for example, and the Unix Epoch (the starting point for counting time as seconds) is similarly on 1st January, albeit in 1970. Even Oracle databases have a date range that starts on 1st January 4712 BCE (and finishes on 31st December 9999). A technical norm or custom of the field as it may be, the use of 1st January as a placeholder date is deeply problematic especially when working with Humanities data, where such a detail could strongly skew the analysis and interpretation of the data. Bishop (2018: 125) worried that “theoretical problems are steamrolled flat by the weight of the data” – how much more concerning then, if that data is not representative of the historical period it claims to capture, but an arbitrary modern convention? 3.2.5 Messy Data

There are two kinds of messy data. The first, as understood by computer and data scientists, is information that is captured as structured, tabular data, but it is riddled with ambiguity, omission, and does not adhere to a standard. In other words, exactly the sort of data that Humanities scholars will work with when compiling their datasets and databases from historical sources. Another challenge presented by historical data is the challenges it presents to optical character recognition (OCR) software in a much more literal take on “messy”. An example of a very simple tabular dataset that combines both types of messy data is from the project Lord Mayor’s Costume Balls in Sydney in 1857 and 1879.11 This prototype project focuses on very structured data: a list of the guests who attended an annual costume ball organised by the Lord Mayor, and their costumes, as published by the Sydney Morning Herald. A digitised version of the primary source12 is available from Trove,13 the National Library of Australia’s digital archive.14 Alongside the portable document format (PDF) of the historical newspaper is the OCR. Table 3.2 illustrates the potential for messiness in even the simplest and most structured of datasets, showing a number of false positives. Note that the messiness remains although at the time of writing, six anonymous volunteers had already contributed to the tidying up of the OCR for this specific page. Academics will invariably discuss the challenges of the messiness of this particular data presents to the process of mapping it ontologically (a word used here specifically in the sense of the Linked Data paradigm and knowledge representation, and not in the sense of a branch of philosophy) and the exploration of

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Table 3.2 Challenges to OCR in the Lord Mayor’s Costume Balls in Sydney in 1857 and 1879 project data OCR from digitised historical document

Text as it appears to human users in the digitised historical document (blurred but legible)

Lewis” Mr. S. H., Italian brigand s h> s ^’f; -Í Levey, Mrs. Philip *’-.*”’” ¿’* Levein, Mrs. J., Norma ‘ * * +*

Lewis, Mr. S. H., Italian brigand Levey, Mrs. Phillip Levein, Mrs. J., Norma Lawson, Miss, Air Lawson, Miss, Night Le Guirè, Mr. – -

that information in a Linked Data system (Nurmikko-Fuller and Pickering, 2021; Gatti, T. et al., 2022). Even with a preliminary investigation having successfully been completed, what remains unclear are issues such as whether the omission of the costume is, itself, a piece of information. The process of recording the names and costumes was possibly one of the announcements, whereby as a guest would arrive, a local government official clerk might (or might not) record their name and their costume, and then announce them (or might not). Are there omissions? Or if so, were they simple mistakes or omissions by the frantically scribing journalists or government officials? Or did the guest perhaps arrive without a costume? And so on. In the absence of additional datasets and pictographic evidence, there is little we can do to address that particular ambiguity, caused by the absence of information. The only solution would be to add to our existing dataset with external information. 3.4

Openness

Openness, accessibility, transparency. All three have something in common but refer to different aspects and outcomes to the way research, data, and code are shared online. Different types of stakeholders are likely to have very different takes on the issue. A politician may welcome the opportunity to share data to show transparency; a researcher may wish to share their data as part of proving the reproducibility of their findings, or disseminate their publications freely to improve their engagement with the academic community, and to support scholarship. Although there are many benefits to freely sharing research (referred to as Open Access), information (Open Data), and code (Open Source), there are also some complicating factors. Not all data can be open (e.g. cultural, societal, privacy-related reasons); Open Access requires a revolutionary paradigm shift for the publication industry; Open Source can be abused and misused, and so on. Conflict arises when seemingly altruistic opportunities for information and knowledge sharing collide with neoliberal and capitalist complexities which nevertheless are an undeniable part of modern societies.

Closed But Not for Business 51 3.4.1 Open Source

In the simplest of terms, “Open Source software” is a term that applies to computer programmes and coding that have been published under free and open licences. This means that the code is (at least to some degree and within the limits of certain conditions) accessible without cost, but also that anyone wanting to use the code can make changes to it. What those limits and conditions are depends on the licensing that the creator of the code has used. Many programmers publish their data on platforms such as GitHub, which in May 2022 had over 83 million developers. The axiom of the Open Source community is that software might be “free as speech, not free as in beer” – an expression attributed to Richard Stallman (Lessig, 2006). The idea behind the slogan is that anyone should be able to access and edit the code (optimistically, the idea is that the code could be improved), as long as those who are editing it similarly share their resulting code openly and for reuse. For example, Creative Commons (CC) licences15 cover a myriad of different types and levels of permissions for intellectual property such as software in the context of copyright laws. Many of these stipulate that the degree of freedom (libre) assigned to the original source code be also applied to any and all derivatives, perpetuating the Open model and encouraging the reuse and further development of existing code, software, and programmes. The application of licences is essential, because (and there are country-specific exceptions to this) copyright (and its restrictions) are applied automatically. Only by opting to use a specific Open licence can a programmer ensure their code is truly ‘free’. But even within the software development community, seemingly a chasm exists between so-called free software and Open Source. Although in some ways fundamentally similar in their drive for accessible software that can be edited and changed by developer (libre), the Free Software Foundation is keen to establish that theirs is one of a philosophical, moral, and intellectual approach: The free software movement campaigns for freedom for the users of computing; it is a movement for freedom and justice. . . When we call software “free”, we mean that it respects the users’ essential freedoms: the freedom to run it, to study and change it, and to redistribute copies with or without changes. This is a matter of freedom, not price, so think of “free speech”, not “free beer”. . . By contrast, the open source idea values mainly practical advantage and does not campaign for principles.16 The difference between these values and the rest of the Open Source community are not easy to establish. What can be said is that the core values of OpenSource.com (what they refer to as the “Open Source Way”) are predominantly practical and pragmatic ones: transparency, collaboration, releasing early and often, inclusive meritocracy, and community.17 From a business model perspective, the freeware movement is the most relevant here. Unlike Open Source code or free software, freeware refers to proprietary software. Examples of the latter are well-known names such as Microsoft Office, or Adobe Creative Cloud – these are software that are the opposite of Open Source,

52 Closed But Not for Business as the creators of the code have applied such exclusively rigid and restrictive rules and licences that users may not and cannot edit the underlying code. These types of software are often also called non-free or closed-source. The requirement for 3 stars in the Five Star Standard for Linked Open Data stipulates the use of non-proprietary data formats. Why then should we consider proprietary software now? The reason for this is that proprietary software has a specific type of Open business model that is applied to it, namely freemium. The core concept here is that, whilst the software is certainly not libre, a simple version (with limited functionality) is gratis. That is to say, there is no monetary cost involved in the acquisition of the software in the first instance, but in order to access the full functionality, or to be entitled to technical support should anything go wrong, the user must pay a fee or enter into a subscription model. Example of the very popular freemium business model include very diverse platforms such as Sketchfab.com,18 LinkedIn,19 and Spotify20 as well as games such as PokemonGo!,21 Minecraft,22 and the Steam platform,23 which offer some free gameplay, but also enable the purchasing of digital artefacts and objects in exchange for real-world money. 3.4.2 Open Access

Where Open Source applies to the software being used for the analysis, Open Access is related to the (academic) research output in terms of peer-reviewed research publications, reports, and papers. Open Access terminologies categorise accessibility on a predominantly two-pronged approach: green, or gold.24 Gold Open Access is not gratis for the author, but it does mean that the publication in its final peerreviewed and copy-edited form is accessible to the research community and the public free of charge (that is to say, it is not behind a paywall, or only accessible to those with a subscription). The drawback or limitation of this approach is that the cost of Gold standard Open Access is often prohibitively high for scholars looking to publish the work they have completed outside of a funded project that has an allocation in the budget for this specific purpose. As an illustrative example, at the time of writing, examples of Gold Open Access rates for one major publisher were between US$500 and US$5,000 per publication.25 Green Open Access, which is also referred to as self-archiving, allows the author to make a (often, but not exclusively pre-print) version of the text available through their personal website, institutional repository, or other similar platform. Copyright restrictions may result in embargo periods or other delays, but ultimately the document is openly accessible. The rules and regulations of specific publishers can also affect which version (e.g. prior to copyediting) of the publication can be made accessible in this way. In recent years, many research funding bodies such as the Australian Research Council (ARC) and the Arts and Humanities Research Council and the Engineering and Physical Sciences Research Council in the UK have mandated Open Source publication of funded projects. This trend is also being reflected by Google Scholar. The function is relatively new at the time of writing: specific matrices are embedded into author profiles, showing which papers are required to be openly accessible, and which Research Council’s requirements must be met in that regard.26

Closed But Not for Business 53 3.4.3 Open Data

Open Data refers to information that has been published online in a way that is accessible to others. It too, like Open Source software, should be published with an Open licence. Open Data meets the criteria of the FAIR data principles (discussed further later), and meets four of the Five Star Standard of Linked Open Data (as seen in Chapter 1). In addition to appropriate licensing, datasets should be published online using non-proprietary software. A common example of this is that a tabular dataset (a spreadsheet) made available as a CSV file is best; sharing it as an MS Excel document is OK, but not ideal; uploading a PDF of that spreadsheet is never the right thing to do, but even that is better than not sharing the data at all! The first mention of the axiom of “information wants to be free”, which is taken as the premise to many a conversation about Open Data, dates to the early 1980s. Now forming part of the vernacular of most practitioners, its origin is attributed to Stewart Brand. So iconic is this anthropomorphic metaphor, that it has its own Wikipedia page.27 The problem here is that the phrase is ambiguous in its exact meaning. The homophone “free” has several meanings, each of which could equally apply for data, and carry strong socio-economic ramifications. “Free” could be “without cost” (gratis), or it could be “without imprisonment or restriction” (libre). Open Data advocates and Web-utopianist data scientists may tout the former, but the transcript of the event in which the phrase was recorded tells a different story: the full quote illustrates that the original focus was on the monetary value of information: In fall 1984, at the first Hackers’ Conference, [Stewart Brand] said in one discussion session: “On the one hand information wants to be expensive, because it’s so valuable. The right information in the right place just changes your life. On the other hand, information wants to be free, because the cost of getting it out is getting lower and lower all the time. So you have these two fighting against each other.” (That was printed in a report/transcript from the conference in the May 1985 *Whole Earth Review*, p. 49.)28 This attitude of data being valuable comes as no surprise to any of us living in the Information Age, and as part of the Data Economy. Yet, the underlying push for Open Data is driven by the idea that data should be accessible by everyone (without monetary cost) and that once in possession of said data, researchers and developers should be able to use, reuse, and republish that data similarly without restrictions. Whilst this may not be an attractive proposition for the personal data of living peoples (as discussed later), there is an increasing push by funding bodies across the globe to make research data as open as possible. Alas, in some cases, there seems to be a lack of clarity as to what the Open policy applies to. The ARC policy, for example, stipulates that “Any Research Outputs arising from an ARC supported research Project must be made openly accessible

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within a twelve (12) month period from the date of publication”, but also states that “Research Outputs do not include research data and research data outputs”.29 This is a challenging contradiction for disciplines such as Digital Humanities in general and Linked Data practitioners in particular, for whom the building, development, structuring, and publication of the research dataset itself constitutes a significant part of the research workflow. In opting to publish information openly and in an accessible manner (as FAIR data, as Open Data), data producers and owners make a deliberate policy decision. The reasons behind this are often centred around transparency, such as with government data as the sites data.gov., data.gov.uk, and data.gov.au all illustrate. These datasets have been used across the globe for myriad purposes, ranging from social benefit by highlighting cyclists’ safety in London, UK (Collins and Graham, 2019); to planning a city in India (Bick et al., 2018); modelling urban infrastructure in Russia (Lantseva and Ivanov, 2016); reconstruction of cultural heritage sites in Cyprus (Themistocleous, 2017); assessing the risk of waterlogging in China (Lin et al., 2018); and mapping slums in Kenya (Mahabir et al., 2020). 3.4.4

Linked Open Data

Linked Data, Linked Open Data, and the Semantic Web represent a cluster of terms that almost refer to the exact same thing, and are thus used synonymously. The key word here though is “almost”. A closer examination and a more thorough deconstruction of the terms highlights fundamental differences between them, in ways that cover technological and philosophical differences. A top-level or broad definition of these terms is given below, followed by a more detailed discussion, which more precisely dwells into the nitty-gritty of these different terms. As the nomenclature would suggest, Linked Data and Linked Open Data are very closely aligned. Both refer to a process, a method, a way of publishing information online by utilising existing Web architecture and technologies. The term “Open” carries with it institutional and legal ramifications, signifying that the data that has been incorporated into this project is free (as in gratis) and be accessed without monetary cost to the data consumer (e.g. not hidden behind a paywall), but also free (as in libre) to be downloaded and even edited by the user. Data can be Linked without being Open (as discussed later in the chapter). Much of the data currently available online is Open, but not Linked (since has not been published according to the Linked Data Five Star Standard – see Section 1.5). On the Semantic Web, this level of granularity of interconnected resources falls short of the aim – here it is the meaning, the value, the content of that resource is captured and presented in a format that makes it meaningful to both humans and software agents alike. In this sense, the Semantic Web is a thing, a collection of things, a manifestation of technological implementation. It is a thing inasmuch as the Web is a thing – in some ways quantifiable, in other ways nebulous, intangible, and perhaps a little incomprehensible as well. If the Semantic Web is a thing, then Linked Data and Linked Open Data are methods. They are a process, or collection of agreed-upon processes, that enables

Closed But Not for Business 55 the current manifestation of the Semantic Web. They do not represent the first or only attempts that have been made to make the Semantic Web manifest, for earlier in the history of the former, the AI community made what ultimately turned out to be unsuccessful attempts to develop just that. Knowledge representation (KR), one of the cornerstones of the Semantic Web, has been discussed for near-on four decades (Levesque, 1984) within the field of AI, which, in turn, has been a field of active research since at least the 1950s. Berners-Lee has discussed Web-facilitated machine interaction for almost three decades (since 1996), and his initial vision for a hypertext system already enabled connections which went beyond simply linking human-readable documents: that is to say, even the earliest imaginations of the Web in the mind of its creator where ones of capturing semantics, and machinereadability has been a desired or intended feature of the system from the beginning (Berners-Lee, 1999). Even the interested general public was introduced to the term more than two decades ago in an article in The Scientific American (Berners-Lee et al., 2001), although there may still be some distance to be covered before we can confidently assert that the Semantic Web has gone fully mainstream. The purpose of publishing information online is that it presents us with an opportunity to capitalise on the value of the data that we have, and to generate further data (as researchers, scholars, academics, curators, etc.). As Linked Data practitioners, we can do this by capturing the information embedded into our datasets; by publishing that information, so that others may encounter it, and benefit from it; by using it as a part of a knowledge graph, which, in turn, enables us to leverage computationally powerful operations such as inferring implicit knowledge from explicitly declared fact, and to enrich existing datasets with information accessible from elsewhere; and by harvesting the benefits – both intentional and unintentional – of using this data, of linking it, and of allowing others to reuse it and build on it. It enables software agents to navigate the data, and to infer connections within it. This connecting of datasets is not ground-breaking per se. After all, clicking on a link of one page and being taken to another is the most basic of human interactions on the Web. Where Linked Open Data differs from that is that rather than being limited to content and hypermedia structures that are navigable by human users, relevant information can be (potentially) identified and connected to anywhere online, and that information, once found, can be incorporated into a process of automated inference. Whether or not this hypothetical ambition has quite been reached yet is another matter entirely. The Five Star Standard is used to refer to Linked Open Data, but the first three stages refer exclusively to standards that should be adhered to with Open Data. Data can be Open (especially when published according to the FAIR data management principles30 of having been made publicly available for findability, access, interoperability, and reuse), without being Linked. It could also be Linked without being Open. Only the four- and five-star levels, which refer to the use of W3C standards of RDF (Reference Description Framework) and SPARQL (SPARQL Protocol and RDF Query Language) respectively can be shown to refer to Linked Data specifically. In summary, if you publish your data online

56 Closed But Not for Business as RDF, and link to other people’s data, you will have reached Five Star Linked Open Data. 3.5

Be FAIR, and CARE

How do we regain agency? How do we recover Data Sovereignty? The option of curtailing data collection is fanciful, and if history tells us anything, it is that legislation and regulation are invariably ineffective. What begins with hot air ends as waste paper. What is needed are clusters of multifaceted solutions, which address both the technical and human considerations. Educating programmers and software engineers to the subtleties of ethics and providing opportunities to learn from past mistakes is but one aspect; diversifying datasets to remove biases (both historical and modern) is another. Ensuring ethical considerations form an integral part of software development, especially with artificial intelligence, deep learning, and machine inference, should take priority in the workflow, rather than be a simple add-on. Alerting users at all levels to be aware of covert data collection, fake news and targeted political marketing, is insufficient on its own; a significant improvement in digital literacy across the population is needed before any real change can be seen. What is necessary then are root and branch changes to the way we interface with the digital world. To this effect, we should consider the FAIR and CARE data principles. FAIR data is Findable, Accessible, Interoperable, and Reusable.31 In this regard, it is a perfect match to Five Star Linked Data: to meet the criteria of Five Stars, data needs to be published online in such a way that it can be interacted with (downloaded, edited, connected to, in other words, reused), which, in turn, necessitates that it is discoverable and openly accessible. The issues of privacy, ethics, and trust are, however, not explicitly addressed in either the Five Star Linked Data Standard nor the FAIR data principles. CARE data principles32 were developed specifically in the context of Indigenous data, and of data sovereignty. CARE explicitly articulates the need for Collective Benefit, Authority to Control, Responsibility, and Ethics.33 These principles were developed in response to FAIR, and in recognition of the fact that Indigenous Peoples are often not part of the decision-making process. The CARE documentation unambiguously states that “the current movement toward open data and open science does not fully engage with Indigenous Peoples’ rights and interests”, and furthermore, that whilst FAIR facilitates data sharing, it does so often among entities who are part of a recognised social, economic, and political elite. In doing so, these principles (risk) ignoring existing power dynamics, and do little to address the danger of perpetuating intellectual colonialism and a Western-only canon for information collection and representation. 3.6 To Be or Not to Be Open? The cultural heritage sector is one where the business models of various institutions have opted for non-Open policies. GLAM are known to charge fees for

Closed But Not for Business 57 researchers and academics for the rights to access and use digital images of the objects in their collections. The British Museum is an example of a complex and almost entirely case-by-case approach to the issue: a student can use an image (at either 72 dpi, which is not a high enough quality for a printout, or 300 dpi) in their doctoral thesis free of cost, but should that thesis be selected for publication, the author must apply for a new permission and pay the associated cost.34 The question then arises as to what exactly is the cost covering? This is an example of a specific business model that seeks to monetise data access at the cost (pun intended!) of Open Access. Another example of institutional policy with a deliberate aim of keeping information inaccessible by the public and the research community is from the GLAM sector in Australia. The story is detailed in Bongiorno (2021), but I will summarise its main points by way of providing context: The National Archives of Australia have two copies of the so-called Palace Letters, a collection of correspondence between the Governor-General John Kerr and Sir Martin Charteris (Queen Elizabeth II’s private secretary) regarding the lead-up to the dismissal of Whitlam’s Labor government in 1975 (one of great controversies of recent Australian political history).35 The second copy was made by David Smith to acquiesce to Kerr’s request: these were deposited by Kerr’s family in the National Archives, with an instrument of deposit that the correspondence be made available to the public in 2005. When the date rolled around, rather than release the material to the public, the National Archives determined that their copies constituted a “personal” collection, and as such were not subject to the Archives Act.36 When taken to court over the issue by Professor Jenny Hocking, so determined were the Archives to keep this material from the public, that they secretly renegotiated a new instrument of deposit with Kerr’s stepdaughter. This locks the material away until at least 2027, and even then it will only accessible to those able to successfully navigate a mine-field of international bureaucracy. But why would an institution such as the National Archives, a self-proclaimed “pro-disclosure organisation”,37 go to court just to prevent Commonwealth records from being accessible by researchers? The Open Source, Open Data, and Open Access communities have all been promoting the notion of openly available information. The idea is not a new one, and has circulated amongst the tech-utopianists for decades. Although the foci of these communities of practice, data standards, and policies are different, they share terminology and ideology. For the Linked Open Data community, the openness of data is in many ways and in many cases a necessary starting point, as is the sharing of new datasets (e.g. RDF datasets) for further linking and interaction by others. There are also a number of popular Open Source software, which are used and recommended (such as Protege)38 by ontology developers and implementers across all disciplinary boundaries. Not all data should be Open. Culturally sensitive data is one example. As discussed in Chapter 2, with data publication come privacy concerns. There are aspects to our medical, personal, and financial information that we might not want to be public knowledge – and who would want their insurance company getting their hands on a complete medical history, frequency of ordered pizza deliveries, alongside perhaps

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one’s tax records, posts detailing one’s social life (including, say habits relating to drinking or smoking), and a comprehensive breakdown of our finances? Why should the state be privy to a complete picture of our social interactions, consumer habits, opinions, preferences? Would we want someone else (anyone else!) to be able to access all the information about us in one comprehensive picture? Where is privacy, if all about us can be known? The case for the preservation of privacy of the individual is relatively easy to make, but what are the considerations when we examine research data? Information that has been generated through the consistent work of academics, for example, and paid for by research council funding, and thus ultimately by the taxpayer. Should we all not be in a position to benefit from access to that data, that information, and that knowledge? As mentioned earlier, there may well be social, cultural, and ethical reasons why not all data should always be immediately completely Open. But what about when political and financial motivation become the driving force behind the decision as to whether or not material is made accessible? 3.7

Solutions Combining Accessible and Inaccessible Data

PARADISEC (Pacific and Regional Archive for Digital Sources in Endangered Cultures)39 is a digital archive, which (at the time of writing) contains 14,000 hours of audio, 2,000 hours of video, and representing 1,281 languages. It has been purposely designed to adhere to international archiving standards, and its goal is to provide access to the material within the archive to members of interested communities. The default for the metadata in PARADISEC is the CC Attribution-ShareAlike 4.0 International Licence, but the access conditions of the project stipulate that “Specific restrictions accompany each item”,40 complicating the picture when it comes to accessing the material in the collection. But how is this possible with a project as large and heterogeneous as PARADISEC, which holds 500 collections? The solution and the unique way in which PARADISEC differs from many of the other examples in this book is that rather than being data-driven in its method for assigning access and restrictions, the approach is one of user-vetting instead. Possible due to the still relatively small user-base of content contributors, the PARADISEC workflows41 allow for a number of different types of engagement and data deposit processes, but it is the registered user to whom the access rights are granted, not the digital resource. Admin and access information, data access conditions, and data access details are assigned to individual users. PARADISEC operates a strict take-down policy. Any item deposited in the archive, if asked by anyone for any reason to be restricted or hidden, will be done so. This reflects the project’s understanding and respect for working with culturally sensitive material included in the PRADISEC archive, which can include content that is categorised as Secret-Sacred, or either Women’s Business or Men’s Business. Since I’m neither an expert, stakeholder, nor a representative of a relevant community, I will not pursue this narrative further. This project is an example of one technical solution to the problem of different degrees of access. It is different from many other examples since it assigns access

Closed But Not for Business 59 rights to individual users, rather than to categories or specific instances of data. For an example of the latter, let’s examine the ElePHãT project, a case study in the amalgamation of data from different sources that are subject to different copyright restrictions and different international laws. 3.8

Case Study: The ElePHãT Project (Bibliographic Metadata)

The ElePHãT project – Early English Print in HathiTrust, Linked Semantic Worksets Prototype – was funded through the Andrew W. Mellon Foundation Workset Creation for Scholarship Analysis project award (Page et al., 2017). The aim of this project was to combine information from two complementary but disparate library collections: the Early English Books Online Text Creation Partnership (EEBOTCP) and the HathiTrust Digital Library (HTDL) (Page and Willcox, 2015). EEBO-TCP is the result of a collaboration between ProQuest42 (a commercial company) and over 150 universities and libraries. It is led by the University of Michigan in the United States, and by the University of Oxford in the UK. The aim of EEBO-TCP is to generate and provide access to fully searchable XML-encoded texts representing the content of the English Short Title Catalogues I and II; the Thomason Tracts; and the Early English Books Tract Supplement, which together constitute a collection of material printed in English between 1473 (a date that constitutes the arrival of the printing press in England) and 1700. At the time of the ElePHãT project, Phase I of the EEBO-TCP was open and free for the public to access. This collection of available data of 25,000 texts constituted the EEBO part of the ElePHãT project. The origins of the HTDL are rooted in a public and legal debate about Open Access. The lengthy process of court cases has been covered from a number of different perspectives (Jones and Janes, 2010; Grimmelmann, 2010; Meyer, 2015), but a short recap here will help illustrate the way in which issues of copyright have restricted access to information, and why the HTDL is still in a complicated position negotiating restrictive copyright on the one side, and their open mission on the other. The HTDL has its origins in the Google Books initiative. Although the public relations (PR) machine of Google claims that Google Books was at the heart of the development of the search engine from its conception in 1996 (inspired by the Stanford Digital Library Project-supported work Sergey Brin and Larry Page carried out as graduate students),43 the large scanning of books in academic libraries by Google’s machines didn’t start until 2004. A year later, they were sued by the Author’s Guild. The process of legal battles took several years – in 2012, a district court ruled that the Google Books initiative and the resulting HTDL constituted a legal form of fair use (a prominently USA-based law which allows limited use of copyrighted material even without the copyright holder’s permission). The University of Michigan was one of the early adopters in terms of their participation in the Google Books project. Theirs was a vision of systematic but also controlled access to the storing and discovery of the academic content held in their institutional libraries. In recent years, the HTDL has grown beyond the core

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of Google Books database, with additional content coming from the tertiary education institutions around the globe that form their participatory listings. Alas, 66% of the HTDL context is under copyright, and access is limited to metadata only (Jett et al., 2016). The niche and very finely curated dataset from the EEBO-TCP was matched, complemented, and enriched by a subset of the collection at the HTDL. At the time, the HTDL – which is itself an amalgamation of the collections of research institutions and libraries in the USA, Europe, and Australia – included over 15 million digitised volumes (as of early 2021, this number had increased to almost 17.5 million).44 This can be broken down to almost 7.5 million book titles, nearly 500,000 serial titles, and a staggering 5.3 billion pages (Page et al., 2017). For the purposes of the ElePHãT project, a specific subset of the HTDL limited to materials that were published in English between 1470 and 1700 was used. Since the material is representative of the collections of many different institutions, the available inherited bibliographic metadata varies between resources based on the institutional standards and policies of the originating libraries (Page and Willcox, 2015). Furthermore, as noted by Jett et al. (2016), both the content of texts and their associated metadata have been produced and OCRd by different institutions using different workflows, and contain idiosyncrasies that make them resistant to any attempts of large-scale, automated processes of standardisation and data tidying. It also results in a number of duplicates and alternatives, which nevertheless have sufficient differences in the metadata to make them appear as unique resources, and are only discovered to be identical once the content is examined. For both the EEBO-TCP and the HTDL collections, the original data was in a non-RDF format. Workflows which have been extensively reported on (Page and Willcox, 2015; Page et al., 2017; Jett et al., 2016) will not be repeated here in great detail. Suffice to say that data held in alternative formats such as relational databases and encoded as XML where converted to RDF using ontological structures that benefitted from Classes and properties (explained further in Chapter 4) from several bibliographic metadata ontologies, such as Bibliographic metadata ontology (MODS)45 and extension to MODS (MADS),46 Bibframe,47 FRBRoo,48 as well as Schema,49 PROV,50 and RO51 (Nurmikko-Fuller et al., 2015; Page et al., 2017). It is important to note that as of February 2020, FRBRoo has been replaced by LRMoo.52 However, since the FRBRoo ontology properties and Classes were used at the time, and any URIs pointing to these concepts and relationships within the ElePHãT project RDF have the FRBRoo ontology prefix. The two aggregated corpora represent very different aspects of the digital library spectrum: EEBO-TCP is a small but open and finely curated dataset; the HTDL subset is vast, with automatically generated metadata records, with considerable restrictions to access. The ElePHãT project is thus a perfect case study example of a project where complementary but disparate datasets were converted into RDF and merged, but the resulting knowledge graph was not completely and comprehensively available to all users. It is an example of how technical solutions could be applied to benefit from the Linked Data paradigm, even when some of the data is Open, and some of it is not. The solution here was to restrict access to some

Closed But Not for Business 61 of the produced RDF by some users. The implemented system architecture reflects this dichotomy of public and privileged data access through the SPARQL endpoint (Page et al., 2017). The copyright restrictions apply only to the deposited material, but not to the metadata. The most valuable aspect of the inclusion of the HTDL data is that it could be used to affect the outcome of the SPARQL queries used to search through the aggregated datasets. That is to say that although the user could not see what the material was in the HTDL, that data would still act as one of the affecting conditions that affected the final results. The user can see that there is something in the HTDL that affected the final outcome of the query, but they cannot see what that something was. In addition to this restrictive policy on the record metadata, the ElePHãT project contains another form of non-open data in the form of JISC Digital Books. The project’s user interface (the Workset Viewer)53 allows users to query the project’s RDF triples: this includes the metadata of records from both the EEBO-TCP and the HTDL, as well as information about the alignments between works, people, and locations in the data. All this is freely and openly accessible, and the workset viewer provides example queries that pull content from both collections. For the EEBO data, if the user is from a subscribing institution, they may also access digital images of the work54 in question from JISC Historical Texts.55 Again, this is a third and separate project, independent of the copyright restrictions imposed upon the HTDL, but limited in accessibility due to institutional policy. The result of the different degrees of openness is that the ElePHãT project results in three separate user interactions based on institutional policies alone. For the sake of clarity of discussion, let’s call the first category unlimited access. This one consists of users whose institution has access to both the HTDL data, and to JISC Historical Texts. Their query will return results that are a list of all the works captured in the ElePHãT project, and their results can also display the digital images of relevant works. The second category of user comes from an institution that has access to the HTDL, but no subscription to JISC Historical Text. We can call this category project access. In this case, the user can access the digital object records in the two collections in the ElePHãT project, but they cannot access the third, external material. The third type of user experiences limited access. This type of user can access the openly available EEBO-TCP data, but cannot see the resources of the HTDL. Since the copyright restrictions apply to the digitised pages, the user will still be able to see the metadata records of each relevant resource, which will enable the user to try to locate the specific work in another repository or collection. Significantly, the restricted content from the HTDL will still feature in the formation of the search results. Taking the “Political Science” example from the EEBOO workset viewer, the results from the HTDL of authors who have written on the topic inform the authors whose works are retrieved from the EEBO-TCP.56 To put it simply, the data from the HTDL which itself remains inaccessible to the user still features as a part for producing the final results that answer the query.

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3.9

Conclusion

Institutional policies and economic models can affect the practical solutions applied to a Linked Data project. This chapter took as its premise that if data does not contain socio-economically, medically, or other personal data (the exposure of which might adversely affect an individual), it should also be Open – but that such levels of access are not always possible, even if desired by the researchers implementing the project. As we have seen in this chapter, institutional policies and economic models can affect how open a given cultural heritage institution makes their collections and data. This consideration, which is entirely removed from the theoretical, philosophical, and even ethical values and premises of the researcher, nevertheless impacts the implementation of Linked (Open) Data. The problems are multiplied in the process of aggregating data from two or more different sources, with different sets of institutional policies, and publishing in a machine-processable format. The practical solutions applied to projects where the involved institutions have different policies are discussed in the context of a case study example, namely the ElePHãT project, which combined bibliographic metadata records from the Early English Books Online Text Creation Partnership with a subset of the HTDL. Linked Data and Linked Open Data are often used synonymously, with the former serving as a shorthand for the latter. There are clear differences though, and they consist of technical, social, and economical aspects, the most fundamental (as reflected in the nomenclature) is the degree (if any!) to which the data is Open (and thus accessible by human users and software agents alike). Institutional policies and economic models can and do affect the practical and pragmatic solutions that researchers and developers can utilise in their projects. There are also different but interconnected institutional policies and economic models that specifically affect the “Open” of the data, and much more so than the “Linked” – it is thus not an issue of either limited (or deliberately limiting) the computational capacity: it is about what data can, should, and will be made accessible, and the (often financial) reasons and motivations behind those decisions. In other words, there are economic considerations which can and do lead to situations where not all information is published as FAIR data, even if it could be, and arguably, should be. This issue is not one that affects RDF and graph databases alone (in fact, most of the projects and examples of such limitations are non-Linked Data projects). Notes 1 Trey Parker and Matt Stone, South Park, Season 7, Episode 9, aired 29 October 2003. http:// edition.cnn.com/TRANSCRIPTS/0007/11/se.01.html and www.mtv.com/news/1121985/ lars-ulrich-tells-senate-committee-napster-hijacked-metallicas-music/. Both accessed 28/05/2022. 2 https://money.cnn.com/2001/07/12/news/napster/ accessed 28/05/22. Tom Lamont, ‘Napster: the day the music was set free’, www.theguardian.com/music/2013/feb/24/ napster-music-free-file-sharing. Accessed 28/05/22. 3 https://public.ccsds.org/pubs/650x0m2.pdf. Accessed 26/01/2023.

Closed But Not for Business 63 4 www.nhmrc.gov.au/about-us/publications/national-statement-ethical-conduct-humanresearch-2007-updated-2018. Accessed 26/01/2023. 5 https://diggingintodata.org/awards. Accessed 26/01/2023. 6 www.cidoc-crm.org/version/version-7.1.1. Accessed 24/08/2021. 7 www.cidoc-crm.org/frbroo/home-0. Accessed 24/08/2021. 8 Scope notes are on p. 77 of www.cidoc-crm.org/sites/default/files/cidoc_crm_v.7.1.1_0. pdf. Accessed 24/08/2021. 9 As earlier, but p. 99. Note that “An instance of E73 Information Object does not depend on a specific physical carrier, which can include human memory, and it can exist on one or more carriers simultaneously”. 10 As above, but p. 80. 11 http://lmb.cdhr.anu.edu.au/. Accessed 03/08/2021. 12 https://trove.nla.gov.au/newspaper/article/13000723/1494784. Accessed 03/08/2021. 13 https://trove.nla.gov.au/. Accessed 03/08/2021. 14 www.nla.gov.au/. Accessed 03/08/2021. 15 https://creativecommons.org/about/cclicenses/. Accessed 01/06/2021. 16 www.gnu.org/philosophy/open-source-misses-the-point.html. Accessed 01/06/2021. 17 https://opensource.com/open-source-way. Accessed 01/06/2021. 18 https://sketchfab.com/. Accessed 01/06/2021. 19 www.linkedin.com/. Accessed 01/06/2021. 20 www.spotify.com/au/. Accessed 01/06/2021. 21 https://pokemongolive.com/en/. Accessed 01/06/2021. 22 www.minecraft.net/fi-fi. Accessed 01/06/2021. 23 https://store.steampowered.com/. Accessed 01/06/2021. 24 There are also other alternatives, such as the Hybrid, Bronze, Diamond/Platinum, and Black. 25 As outlined at https://service.elsevier.com/app/answers/detail/a_id/5972/supporthub/ publishing/~/what-does-it-cost-to-publish-gold-open-access%3F/#:~:text=Please%20 read%20more%20on%20full,from%20500%20%2D%205000%20US%20dollars. Accessed 01/06/2021. 26 https://scholar.google.com/intl/en/scholar/citations.html?1#publicaccess. Accessed 01/06/2021. 27 https://en.wikipedia.org/wiki/Information_wants_to_be_free. Accessed 07/05/2021. 28 www.rogerclarke.com/II/IWtbF.html. Accessed 06/05/2021. 29 www.arc.gov.au/policies-strategies/policy/arc-open-access-policy. Accessed 07/05/2021. 30 www.ands.org.au/working-with-data/fairdata. Accessed 27/05/2021. 31 www.ands.org.au/working-with-data/fairdata. Accessed 25/02/2021. 32 www.gida-global.org/care. Accessed 25/02/2021. 33 The CARE Principles for Indigenous Data Governance can be downloaded in full from https://static1.squarespace.com/static/5d3799de845604000199cd24/t/5da9f4479ecab2 21ce848fb2/1571419335217/CARE+Principles_One+Pagers+FINAL_Oct_17_2019. pdf. Accessed 25/02/2021. 34 www.britishmuseum.org/terms-use/copyright-and-permissions. Accessed 27/05/2021. 35 www.aph.gov.au/About_Parliament/Parliamentary_Departments/Parliamentary_ Library/FlagPost/2020/November/The_dismissal. Accessed 28/05/2021. 36 www.legislation.gov.au/Details/C2019C00005. Accessed 28/05/2021. 37 www.naa.gov.au/about-us/media-and-publications/media-releases/hocking-v-dgnational-archives-australia#:~:text=‘The%20National%20Archives%20is%20 a,compelling%20need%20to%20withhold%20it. Accessed 28/05/2021. 38 https://protegewiki.stanford.edu/wiki/Main_Page. Accessed 07/05/2021. 39 www.paradisec.org.au/. Accessed 02/06/2021. 40 www.paradisec.org.au/deposit/access-conditions/. Accessed 02/06/2021. 41 https://paradisec-archive.github.io/PARADISEC_workflows/. Accessed 07/06/2021. 42 www.proquest.com/index. Accessed 06/05/2021.

64 43 44 45 46 47 48 49 50 51 52 53 54 55 56

Closed But Not for Business https://books.google.com/googlebooks/about/history.html. Accessed 07/06/2021. www.hathitrust.org/about. Accessed 07/05/2021. www.loc.gov/standards/mods/modsrdf/. Accessed 07/05/2021. https://id.loc.gov/ontologies/madsrdf/v1.html. Accessed 07/05/2021. www.loc.gov/bibframe/. Accessed 07/05/2021. www.cidoc-crm.org/frbroo/home-0. Accessed 07/05/2021. Since February 2020, FRBRoo has been replaced by LRMoo (https://cidoc-crm.org/frbroo/ModelVersion/lrmoo-f.k.a.frbroo-v.0.6). Accessed 30/01/2023. https://schema.org/. Accessed 07/05/2021. www.w3.org/TR/prov-o/. Accessed 07/05/2021. https://wf4ever.github.io/ro/2016-01-28/. Accessed 07/05/2021. https://cidoc-crm.org/frbroo/ModelVersion/lrmoo-f.k.a.-frbroo-v.0.6. Accessed 30/01/2023. http://eeboo.oerc.ox.ac.uk/. Accessed 06/06/2021. An example of this is at https://data.historicaltexts.jisc.ac.uk/view?pubId=eebo-ocm122 66003e, but the page is not accessible to the public nor to those whose academic institutions do not subscribe. https://historicaltexts.jisc.ac.uk/. Accessed 07/06/2021. The query translates to: “Select all works from the EEBO-TCP, which have been written by authors who have also published on the topic of ‘Political Science’ in the HTDL”. This reflects the presence of the genre in the HTDL, but not in the EEBO-TCP.

Bibliography Berners-Lee, T. (1999). Weaving the Web: The Past, Present and Future of the World Wide Web by Its Inventor. Orion Publishing Co. Berners-Lee, T., Hendler, J., and Lassila, O. (2001). “The Semantic Web”. Scientific American, 284(5), pp. 34–43. Bick, I. A., Bardhan, R., and Beaubois, T. (2018). “Applying Fuzzy Logic to Open Data for Sustainable Development Decision-Making: A Case Study of the Planned City Amaravati”. Natural Hazards, 91(3), pp. 1317–1339. Bishop, C. (2018). “Against Digital Art History”. International Journal for Digital Art History, 3(July). Bongiorno, F. (2021). “The Wait of History”. Inside Story, May 7, 2021. https://insidestory. org.au/the-wait-of-history/ Collins, D. J., and Graham, D. J. (2019). “Use of Open Data to Assess Cyclist Safety In London”. Transportation Research Record, 2673(4), pp. 27–35. Cunningham, G. (2013). “The Sumerian Language”. In Crawford, H. (ed). The Sumerian World. Routledge. Gatti, T., Nurmikko-Fuller, T., Pickering, P., and Swift, B. (2022). “Having a Ball: A Linked Data Approach to Fancy Dress in Colonial Australia”. Proceedings of the International Digital Humanities Conference 2022 (DH2022), Tokyo, Japan, 27–29 July. Greenfield, A. (2017). Radical Technologies: The Design of Everyday Life. Verso. Grimmelmann, J. (2010). “The Elephantine Google Books Settlement”. Journal of the Copyright Society, USA, 58, pp. 497–520. Huxley, A. (1932). Brave New World. Longman. Jett, J., Nurmikko-Fuller, T., Cole, T. W., Page, K. R., and Downie, J. S. (2016). “Enhancing Scholarly Use of Digital Libraries: A Comparative Survey and Review of Bibliographic Metadata Ontologies”. Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries, Newark, USA, 19–23 June. Jones, E. A., and Janes, J. W. (2010). “Anonymity in a World of Digital Books: Google Books, Privacy, and the Freedom to Read”. Policy & Internet, 2(4), pp. 43–75.

Closed But Not for Business 65 Lantseva, A. A., and Ivanov, S. V. (2016). “Modeling Transport Accessibility With Open Data: Case Study of St. Petersburg”. Procedia Computer Science, 101, pp. 197–206. Lessig, L. (2006). “Free, as in Beer”. WIRED, 9 January 2006. www.wired.com/2006/09/ free-as-in-beer/. Levesque, H. J. (1984). “Foundations of a Functional Approach to Knowledge Representation”. Artificial Intelligence, 23, pp. 155–212. Lin, T., Liu, X., Song, J., Zhang, G., Jia, Y., Tu, Z., . . . and Liu, C. (2018). “Urban Waterlogging Risk Assessment Based on Internet Open Data: A Case Study in China”. Habitat International, 71, pp. 88–96. Mahabir, R., Agouris, P., Stefanidis, A., Croitoru, A., and Crooks, A. T. (2020). “Detecting and Mapping Slums Using Open Data: A Case Study in Kenya”. International Journal of Digital Earth, 13(6), pp. 683–707. Meyer, R. (2015). “After 10 Years, Google Books Is Legal”. The Atlantic, October 20. Nurmikko-Fuller, T., Page, K. R., Willcox, P., Jett, J., Maden, C., Cole, T., . . . and Downie, J. S. (2015). “Building Complex Research Collections in Digital Libraries: A Survey of Ontology Implications”. Proceedings of the 15th ACM/IEEE-CS Joint Conference on Digital Libraries Knoxville, USA, 21–25 June. Nurmikko-Fuller, T., and Pickering, P. (2021). “Reductio ad Absurdum?: From Analogue Hypertext to Digital Humanities”. Proceedings of the 32nd ACM Conference on Hypertext and Social Media (ACMHT21), Dublin, Ireland, 30 August–2 September. Page, K. R., Nurmikko-Fuller, T., Cole, T., and Downie, J. S. (2017). “Building Worksets for Scholarship by Linking Complementary Corpora”. Proceedings of the International Digital Humanities Conference 2017 (DH17), Montreal, Canada, 8–11 August. Page, K. R., and Willcox, P. (2015). ElEPHãT: Early English Print in the HathiTrust, a Linked Semantic Worksets Prototype. Final Report for Workset Creation for Scholarly Analysis: Prototyping Project, University of Illinois at Urbana-Champaign. Schöch, C. (2013). “Big? Smart? Clean? Messy? Data in the Humanities”. Journal of Digital Humanities, 2(3), pp. 2–13. Taylor, J. (2013). “Administrators and Scholars: The First Scribes”. In Crawford, H. (ed). The Sumerian World. Routledge. Themistocleous, K. (2017). “Model Reconstruction for 3D Visualization of Cultural Heritage Sites Using Open Data From Social Media: The Case Study of Soli, Cyprus”. Journal of Archaeological Science: Reports, 14, pp. 774–781. van Hooland, S., and Verborgh, R. (2014). Linked Data for Libraries, Archives and Museums: How to Clean, Link and Publish Your Metadata. Facet Publishing. Zeng, M. L. (2019). “Semantic Enrichment for Enhancing LAM Data and Supporting Digital Humanities. Review Article”. El Profesional de la Información, 28(1), p. e280103.

4

4.1

“Truth” and Bias

Preamble

In “Good Omens”, Pratchett and Gaiman (1990: 17) provide a delightful observation of the subjectivity of winks. An exchange between nuns in their botched attempt to swap the newborn baby of an American diplomat for the Antichrist seemed humorous enough, but it’s genius is in conveying the idea that even something as simple as a wink is necessarily subject to specific interpretation, with potential disagreement over meaning, and the possibility of ambiguity between communicating co-conspirators. The academic value of this concept emerged from an entirely unpredicted source: Katrina Grant’s 2022 work “Landscape and the Arts in Early Modern Italy” outlines the significance of the wink as a case study of anthropological investigations, ground in a robust theoretical framework. Enter Clifford Geertz (1973: ff 6), and his description of the wink as mode of communication. He surmises a scenario – a thought experiment, if you will – of two boys who are manifesting the same physical movement (“rapidly contracting their eyelids of their right eye”), but whilst for one it is an involuntary twitch, for the other, it is a wink. A deliberate, conspiratorial, socially determined way of passing on information. There are thus two completely valid and accurate descriptions of the same phenomena – the “truth” and accuracy of the label applied to the eye-lid twitching is context-dependent, and requires additional knowledge about the boys, their activities, aims, modes of social interaction, and myriad other considerations. It is reminiscent of the “6–9” meme.1 It depicts two men discussing graffiti on the ground. The man on the left proclaims it to be the number six; the man on the right, seeing it from the opposite direction, insists it is a nine. Thus, we see that perspective is important; we all have preconceptions, and our understanding of facts is neither neutral nor objective. It’s not just beauty that’s in the eye of the beholder but also the wink, blink, or twitch. Googling for the 6–9 meme will bring up a myriad of interpretations of the same image by Web users across the globe. One of the more advanced is a discussion between two contributors and published on the second discussant’s blog.2 The first editor declares that the two men’s views cannot in fact be both equally correct, for there are pieces of contextual information such as the intention of the original graffiti artist (who will have known which number they were depicting) as well as DOI: 10.4324/9781003197898-4

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details such as the orientation of the road, the locations of nearby buildings, and so forth. They lament the lack of willingness of people to do research, in their rush to just be right. The second author has chimed in to counter this argument, stating that it is rare that we can omnisciently discuss the motivations of others, and that many things we observe are not the result of a deliberate cognitive process. The problem, he says, is that we believe in the possibility of objective observation and analysis, when those cannot exist. And yet, much of knowledge representation and database development seems to have that exact aim, or user expectation. Computational technologies, digital databases, and algorithms seem to enjoy a status of objectivity – because it’s a computer, it doesn’t care or have opinion, so it can’t be biased. This assumption, one that sometimes plays a covert role in our thinking, is a falsehood. Every system, every algorithm, every dataset contains some decisions that were at some point made by someone. That is not to say that digital tools or their creations are motivated by exclusively nefarious agendas, but if there is one common thread woven throughout this book, it is the recognition of the universal truth of Kranzberg’s (1986: 545) First Law: “Technology is neither good nor bad; nor is it neutral”. To apply this idea to the specific context of Digital Humanities, and to Linked Data in particular, we need to have a closer look at information representation as part of the process. For the purposes of ontological modelling and the Linked Data paradigm, the recognition of the impossibility of a completely objective understanding of the universe (and everything in it) is absolutely paramount, but how actively is this critique or understanding applied to the process? Ultimately, what we must contend with is the possibility that we have insufficient information to determine whether it is a wink or a blink. Perhaps it is a matter of opinion or perspective: one man’s blink is another man’s wink. Thus, we should strive for models that enable the representation of both. Sure, this makes the model more complex (which has the problem of being computationally more expensive, but also more difficult for a human user to comprehend and use). But what’s a little complexity when the representation of truth is at stake? 4.2

Ontologies

Perhaps the most famous of definitions for ontologies is the one from Gruber (1993) that we encountered in Chapter 1: “An ontology is an explicit specification of a conceptualization”. The sentence is thrilling in its snappy delivery but also impenetrable. What, exactly, is a conceptualisation, and what is a specification of such a conceptualisation? And does the possibility of an explicit specification imply the possibility of a vague one? Building on Henry Ward Beecher’s metaphor of words as pegs on which ideas are hung, Hyvönen (2018: 150) introduces the concept of ontologies as pegs from which words are hung. Ontologies have been declared as being “crucial to the Semantic Web” by Wilks and Brewster (2009: 2). DuCharme (2013: 39) describes them as “formal definitions of vocabularies that allow you to define complex structures as well as new relationships between your vocabulary terms and between

68 “Truth” and Bias members of the classes that you define”, addressing both the schema-level and instance-level functionality of ontologies. For me, ontologies are formalised structures that act as information templates – that is to say, they are machine-readable documents, written in RDF, which explicitly state all the possible types of things that can exist in the domain that the ontology is mapping (such as people, places, and events perhaps), as well as the possible relationships between those entities (a person might be born in a place, or a person might have attended an event). They define the rules, which determine those RDF triples that are possible (and if you so choose, those that are not). They come with the promise of enabling automated inference, the process of bringing forth implicit information and connections within a dataset, by deducing new links that indirectly exist between explicitly declared facts. The aim of an ontology is to provide a formalisation that represents the knowledge within and about a specific domain. For this reason, ontologies can be subjective, or include overt or covert biases of the ways in which the people who designed them see the world. Ontologies consist of Classes and properties. There is no technological reason for this, but the human-readable convention that some of us choose to use is that the former are written with capital letters, and the latter are all lower case. This maps onto the structure of the triple as well: Subjects and Objects are instances of Classes, and predicates are properties. Ontologies are commonly visualised as a type of network graphs: where the latter have nodes and arcs, the former have circles and lines. The circles are Classes, the lines are properties. Ideally, these lines are in fact directed arrows that show the order of the triple: properties always run from a Domain to a Range. The former is a Class for which a given property is specifically defined, the latter comprises all possible values for that property. Ontologies are decentralised and non-hierarchical. They can be navigated from any starting point. The only hierarchical structures that ontologies exhibit are the hypernyms and hyponyms of Classes and properties: Classes can have superClasses and subClasses, and properties can have superproperties and subproperties. These relationships express an increasing granularity or specialisation, and are perhaps best explained via a mnemonic: “All lions are animals, but not all animals are lions”. This simple rule explains the relationship between superClasses (animals) and subclasses (lions). The key here is that lions will have all the characteristics (or properties) that all animals have, but there will be some other animals, which do not share all of the characteristics of a lion (a herbivore, for example, will have a different kind of diet, different kind of teeth, etc.). Similarly, subClasses inherit all the properties from their superClass, so in this example, we would not have to define for all animals separately that they need air, water, a habitat, they mate, they have life spans, etc. Although no taxonomy of ontologies exists, they are broadly divisible into four main categories: domain-specific (or domain ontologies), upper (or general ontologies), representation, and application ontologies. Of these, the first is focused on the representation of a specific subset of the world, such as a discipline or area of expertise. For scholars of the Humanities and practitioners of the GLAM sector, the

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most well-known is arguably the CIDOC CRM.3 This particular conceptual reference model has been extended to include many subdomains, such as (inter alia) provenance metadata4 and social phenomena.5 It has also been applied successfully in the context of the Linked Art community, who have developed the Linked Art Model as a simple and more easily applied core of the complex CIDOC CRM.6 Upper ontologies on the other hand are designed to model those entities and aspects that are a common occurrence, universally. Examples of these might include WordNet, which is based on the English lexicon and where the concepts number in their hundreds of thousands. Semantic Web languages, such as OWL and RDFS (RDF Schema), are representation ontologies, and are used for low-level capture (and indeed often the structure of the ontology itself), whilst ontologies built to meet the needs of a specific application are examples of the final category. Ontologies can vary in their design philosophy. Brewster and O’Hara (2004) quote Steve Fuller, who divides ontologies into two main categories: the Newtonian (a reductionist, more descriptive model) and the Leibnizian (which is more concerned with the capture of the nuanced complexities of experience). The benefit of the former is that it is easier to control and administer – the latter is fuzzier but may be thus easier to apply. A Newtonian philosophy regarding ontology design necessitates a degree of omniscience over the subject domain, but those of the Liebnizian school of thought are perhaps more likely to argue that since true objectivity is unachievable, a data-driven approach is the more truthful of the two (Nurmikko-Fuller, 2018: 348). Undeniably and by their very nature, ontologies present a reflection of ways in which those people who design them perceive reality. Idiosyncratic and culturally conditioned values can affect the structure and design of an ontology. Consider, for example, the complex issue of sex and gender (which is a diverse and nuanced spectrum insufficiently captured by the binary of male and female), or the perception that time is a linear construct. Since ontologies reflect the world as interpreted by those who design them, a hermeneutic approach to evaluating the embedded design decisions becomes nothing short of essential. It is not a universally agreed fact that ontologies are strictly necessary for the implementations of Linked Data projects. DuCharme (2013: 310) notes that providing “a set of property declarations with no class declarations is actually quite common because [it enables people to] share their vocabularies for reuse without getting involved in more structural declarations of classes and their relationships”. Whether or not they are strictly necessary for a Linked Data or Linked Open Data implementation, any “working ontologist” (Allemang and Hendler, 2011) will find them a useful solution for gracefully navigating and scaling the complexities of the (digital) Humanities datasets they work with. 4.3

Bias in Ontologies

But wait, there’s more to Geertz’s wink. Not satisfied with the complexity of the question with regard to just two boys, he proposes a third, who engages in the activity with malicious intent. The examples become increasingly complex, to build

70 “Truth” and Bias the argument that although all boys are manifesting the same behaviour, observation of the eye moment alone is insufficient in the understanding of the meaning of that behaviour. Western perspectives have permeated almost every aspect of information capture, representation, and analysis. From Linnaean taxonomy to the inherently racist roots of Statistics, to Google search results, Western ways of categorising the world have become the only acceptable voice of authority. Much of modern technological development, and especially digital and computation development is (right or wrongly, given the facilities and outputs of Zhongguancun in the Beijing-TianjinShijiazhuang Hi-Tech Industrial Belt) accredited to the Silicon Valley of the San Francisco Bay Area in Northern California.7 The truth is the history of Computer Science, both of software and hardware, has been more diverse than the image of the modern WASP: this white, Anglo-Saxon Protestant is a privileged, cisgender, heterosexual, atheist male. These men have decided between a wink and a twitch: it is in their eye that the beauty8 is beheld. Silicon Valley and the tech giants that are driven by middle-aged white men (Tim Cook for Apple, Bill Gates for Microsoft, the slightly younger Mark Zuckerberg for Facebook, Ted Sarandos and Reed Hastings for Netflix, etc.) have come under criticism for creating a society that the Guardian describes as a networked world dominated by an industry that oozes tech-bro arrogance and affluence combined with a profound ignorance of what life is like for most people. . . The tech elites who create the products and services are unlikely to have experienced social exclusion, racism, misogyny, poverty or physical abuse. And in particular they have little idea of what life is like for women . . . It’s hard to avoid the conclusion that they still see . . . say, hate speech, as a PR problem to be managed rather than as a structural issue that requires radical reform.9 Cultural background, lived experience, personal preference, prior knowledge, and myriad other considerations form part of our perception of the world and how we categorise what we encounter. And, just as our own internal biases affect the way we interpret information, so too do the design decisions that have gone into the development of digital information storages. In a similar way, the histories of cultural heritage organisations and scholarly traditions influence the perceived and articulated needs and scope of databases utilised in the sector. Natural History museums for example tend to favour hierarchical structures that work well with the Linnaean taxonomies working their way down from domains and kingdoms down to individual species, but why should we assume a similar structure would be appropriate for recording, say anthropological data, or the social networks used for prosopography? It is important to remember that even this seemingly scientifically rigorous approach is prone to error, such as the misclassification by Linnaeus himself (Witteveen and Müller-Wille, 2020). The process of ontological modelling for the Semantic Web, or for Linked Data, is all about identifying and representing reality. It is the process of identifying

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data categories (such as people or places) and the relationships between them. It is thus the very epitome of subjectivity. A simple thought experiment illustrates the point: if you are not aware of the possibility of a wink as a form of interpersonal communication, all instances of an eye movement could only ever be a variant of a blink. If one were to create an ontology of eye-movement reasons, one might feasibly utilise qualitative research methods and interview the boys to assess their reasons, aims, and activities, and situate that in a socio-culturally accurate context. But what to do when there are no people to interview? The assertion of an interpretative lens when cast on historical and archaeological material has this exact challenge. The risk here is of the temptation to fill in the blanks, or to interpret finds or historical practice through our modern values, cultural practices, and ethical, legal, and social norms of our times. The desire or tendency to do so can be completely covert (so much so, that we are not aware of it ourselves). There is also a practical and pragmatic line to be drawn somewhere: there is a difference in the complexity of the model, and thus its relative usability, depending on how detailed the granularity of the fact that’s being represented. We might think it’s reasonable to differentiate between different kinds of people, for example, based say, on their job description (doctor, dentist, cleaner, software developer) or their role in society (king, peasant, tax payer), but we might take it for granted that all these people operate within a linear space-time continuum. The decision as to which aspects of our reality to capture is driven by the knowledge, aims, skills, and decisions of the person creating the ontological model. It is thus inherently subjective. 4.4

Document Your Design

Ontologies can and do exist for an immensely wide range of topics. These certainly include domain-specific ones, which focus on a given discipline or area of expertise, such as the CIDOC CRM10 for museum data; LRMoo11 for libraries; or the Music Ontology,12 which, as the nomenclature would suggest, maps information about music. But there are also other ontologies that tackle much more abstract notions: the Event13 ontology, for example. Similarly, Provenance Ontology (PROV-O)14 maps processes, in this case of data provenance. At the very most abstract end of the spectrum are RDF Schema15 and OWL,16 which do not map concepts but the concept of a concept – for example, owl:Class defines a given idea as an entity, rather than a property (a relationship between entities). More than one ontology can be the “correct” one in terms of being applicable to a resource, depending on what the aim of the mapping is: you could have one ontology for capturing physiological movements of the facial muscles and another for facial expressions as a mode of communication. RDF as a technology is very robust to change, and it is also very easy to add enriching information by equating Classes and properties from one ontology to respective ones in others – technologically. That is to say, you can simply add an additional RDF triple to

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equate a Class with another (both ontologies might reasonably have a separate Class for both a twitch and a wink), but knowing that these two Classes in these two different ontologies refer to the same thing is an intellectually challenging and time-consuming task. The documentation of design decisions thus becomes of the utmost importance. Unfortunately, documenting is a labour-intensive, boring, time-consuming, and utterly under-appreciated task. This can be particularly true in those instances where the data category (Class) or characteristic (property) is very familiar conceptually to the person responsible for the documentation. “But this is obvious!” we exclaim and throw our hands in the air. Or perhaps we internalise this frustration, and it only manifests as an involuntary muscle twitch in the eye. The ontologies developed by the Library of Congress for capturing bibliographic metadata illustrate both excellent and appalling approaches to documentation. Remembering that the aim of the documentation is to describe each Class and property with sufficient information to enable someone else (a human scholar!) to decide whether or not the Class in question matches the requirements of their data, we can compare and contrast the madsrdf:Title from the MADSRDF ontology,17 with bf:Title from Bibframe18 (both are by the Library of Congress). The Classes refer to the same concept, but the difference in documentation is notable (Table 4.1). Insufficient documentation means users can less confidently assert equivalence to their data, or to other ontologies, but good quality documentation is time-consuming. It would seem though that since the fundamental premise of Linked Data is to facilitate information exchange, increased interlinking, and improved discoverability, it is worth investing that time. The reuse of ontologies defined in the context of other projects can fall prey to similar concerns. Whilst there are myriad reasons for the reuse of existing ontologies (benefitting from the insights of experts, time-effective use of an existing resource, increased discoverability, etc.), unclear or limited (and in some cases even omitted) documentation of the ontology risks the incorrect reuse of a Class or property. Perhaps defaulting into the reuse of a suboptimal but known ontology is more likely, for reasons identical to those for the picking and choosing of triplestores: familiarising oneself with an ontology is time-consuming and intellectually challenging. There are clear benefits for the decision to use a known and popular ontology, but those decisions do not reflect an objective evaluation of all possible ontologies (or even all known ontologies). Table 4.1 The difference in the documentation of two ontologies with the same Class of Title, both from the Library of Congress Class Scope note

www.loc.gov/mads/rdf/v1#Title “Describes a resource whose label represents a title”.

http://id.loc.gov/ontologies/bibframe/Title “Title information relating to a resource: work title, preferred title, instance title, transcribed title, translated title, variant form of title, etc.”

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Justify Your Choices

Seeing bias in ontological modelling can be relatively easy if it pertains to perspectives or associations that are part of our daily thinking and discourse. Observing a gender bias, for example, might come more readily to a researcher working on gender issues; class theory might be evident to Marxist scholars. One of the challenges of working with information that captures and represents events of the distant past is the constant need to determinedly avoid succumbing to the temptation of presentism. This can be challenging without a healthy degree of self-criticism and introspective evaluation – nevertheless, it can be tricky to avoid the uncritical application and adherence to modern-day moral, ethical, social, and legal standards, attitude, and opinions. Examples of issues where we might struggle to do so are historical examples of cultural and social practices which we do not agree with, such as human sacrifice, child brides, or slavery. Although tackling complex issues, these examples have a relative simplicity to them as they constitute extreme examples for which many of us will hold deeply seated and unambiguous views – certainly they are topics that few us remain ambivalent about, but they are also ones that show a diachronic shift from social acceptance and even active promotion to the large scale condemning and prohibition of an act or practice: what was once acceptable and good is now abhorrent and bad, or vice versa. The manifestation of issues such as the lens of presentism is easy to spot if we condemn practices which were once socially acceptable but now to us are not in our historical analysis, and similarly so if we build this value judgement into our ontological models. There are also other more ambiguous cases, where the risk of presentism is less clear, or not as easily distinguished from contemporary cultural perspectives. Examples might include the prohibition of the consumption of pork, which has long roots in some cultural contexts, but is not universally observed. Can we compare that to relatively recent changes in some cultural context of say, the banning of smoking in public and confined spaces? And will future generations judge us for the consumption of sugar, the use of palm oil, and other cultural practices which might seem innocuous to us now (leaving the lights on, letting the tap run, choosing to drive short distances)? And there is also the possibility of intangible heritage that has been lost and is thus unknown to us: would it not be impossible to build an ontological model that captured a fourth option beyond a wink, a blink, and a twitch if those three are the only options we know of? In a similar way to how we must resist the temptation of applying modern moral and ethical views to ancient practices, so too should we consciously try to avoid categorising the ancient world into knowledge categories that make sense to us today. The Linnaean taxonomy of plants is unlikely to map easily to an ancient categorisation of herbs and flora, and forcing that categorisation risks the loss of tacit knowledge, inferable from the context in which different plants may occur, for example. That having been said, the Linnaean model itself is a product of a historical period and is not entirely without flaw or historical bias. Another rather tragic example of this is attempts by Banks to apply the Linnaean categorisation to Australian flora in 1777 – and in doing so forcing the loss of Indigenous knowledge accumulated over millennia.

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By way of an example which is easy to relate to and which has enjoyed some time in the public limelight already is the notion of gender binary, which is not, although insisted by many non-experts, based on scientific facts. Human genetics are much more complex than a division into just XX or XY chromosomes, and even the notion that the gender binary as a traditional social construct is broken as soon as we examine this categorisation outside of the Western taxonomies. We see this categorisation everywhere on a daily basis although not necessarily ubiquitous – but it is in legal documents; it is reflected in architecture of buildings in the form of toilets and changing rooms; it determines the arrangements of clothes in stores; it is reflected in the colour coding of products from pens to personal hygiene. And indeed there are digital ontologies which categorise all human persons into either Female or Male exclusively. An example of this is the Networked Environment for Personalized, Ontology-Based Management of Unified Knowledge (NEPOMUK) Contact Ontology, which describes a topic as innocuous as contact information. Established in 2007 and updated in 2013, the NEPOMUK ontology explicitly and exclusively only permits two genders, nco:female and nco:male. The documentation specifies that “instances of this Class may include male and female”.19 Rather than a conscious attack on gender minorities, or the deliberate perpetuation of the binary, this seems a likely example of unchecked bias. Since genre is by no means the main focus of this ontology, the issue has been solved quickly, efficiently, and, arguably, with minimal thought and evaluation. But not with malice. Other ontologies such as the Friend of a Friend (better known by its acronym FOAF, pronounced “foafh”),20 one of the most frequently used ontologies, opts not to explicitly map gender diversity, but rather leaves it open to the user: foaf:gender is commented with the gender of this Agent (typically but not necessarily “male” or “female”). . . In most cases the value will be the string “female” or “male”. . . Values other than “male” and “female” may be used, but are not enumerated here. The gender mechanism is not intended to capture the full variety of biological, social and sexual concepts associated with the word “gender”.21 Recent social interest and the rise of the LGBTQI+ community’s voice has seen this categorisation change, and indeed there are ontologies such as Homosaurus,22 which has made an attempt to include a greater degree of diversity in its categorisations of people. This ontology includes non-Western approaches to gender, including terms from at least Samoan, Tongan, Italian, Dominican, Indigenous Canadian (specifically Ojibwe), Hindi, Native American (specifically Mohave), Urdu, and Turkish to diversify and enrich the gender binary to cover not just three but many other genders as well, including representing gender fluidity and thus enabling an idea that gender may not be set and immutable. But this is just one ontology, and one that has not yet enjoyed wide-scale adoption to information representation. But, certainly it is an example of new things to come, perhaps the first of many different models capturing rich and diverse perspectives onto the categories used to define and understand human groupings in the world.

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What is important to note here is, whether adhering to the gender binary, or deliberately breaking it, the designers of these ontologies were driven by a desire and aim to capture the truth of the world as they see it, experience it, and know it. There is thus an unavoidable bias in the design of the ontology itself, which can be difficult or even impossible to spot until one is explicitly confronted by a contradicting and contrasting viewpoint. In other situations, presentism might creep in unobservable and unchallenged. It might be present but undetectable because the values or terminologies in use have not changed. It could be easier to spot in a historical context, removed from our own. An example of the latter is the interpretation of the Mesopotamian archaeological material through a Biblical lens: consider for example Woolley’s 1923 publication “Ur of the Chaldees” or his naming each of the near-identical pair of figurines of goats found at the Royal Tombs of Ur as a “Ram in the Thicket” (a reference to the motif in Genesis). It appears the naming conventions are less of an accidental interpretation and more of a deliberate display of knowledge of biblical themes: an ophthalmologist sees ocular myokymia in every twitch, wink, and blink. 4.6

Case Study: Old Babylonian Literature

The following case study example describes a Linked Data project for capturing the narratives of literary compositions23 considered to be mythological in genre, written in Sumerian24 by the scribes of ancient Babylon around 2500 BC – a context spatio-temporally, culturally, and linguistically far removed from modern-day hubs of technological development. Linked Data and ancient Sumerian literary compositions can be successfully paired (Nurmikko-Fuller, 2014, 2018): applying ontologies to content written some 4,500 years ago enables us to test the robustness and suitability of modern ontological structures to adequately capture and represent ancient world data. But in doing so, we must be hypervigilant in not inadvertently superimposing modern conventions and perspectives onto ancient world data – or, at the very least, critically assess whether it is even possible to use modern ontological structures, built by modern scholars with modern views to represent modern data in this ancient world context. What we have here is the juxtaposition of a dataset that is characterised by all the attributes of Humanities data: the corpora are rich and heterogeneous in type, genre; the texts themselves are often broken, tablets are missing or damaged, and the twin accidents of preservation and discovery mean that what we do have is a scattered and incomplete sample of what once was. This unstructured data, riddled with linguistic and historical uncertainty and ambiguity, offers an incredible opportunity for us to test the limits of the Linked Data methodology. It is positioned to support existing research paradigms in many ways (such as through the aggregations of collections and data from cultural heritage institutions) but can also enable a wholly new set of research questions especially at the periphery of disciplines (such as the exchange of ideas in Hellenistic Babylonia with the other cultures of the Mediterranean).

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The composition could be studied from the perspective of several different domains: narratology (e.g. Propp, 1968; Campbell, 2008), object biography and bibliographic metadata, as well as Assyriology. The challenge here was to assess whether existing Linked Data tools (namely, existing ontologies developed and designed to capture those domains) could be used to adequately represent all those aspects without succumbing to a reductionist approach or shoe-horning ancient concepts into modern ontologies. The Three Ox-drivers of Adab25 has a non-linear narrative structure, which would make the narrative arc an interesting challenge to map (in other words, should events be represented in a linear fashion, or are the narrative devices of a frame story and or a flashback significant enough attributes to warrant representation in the ontological model?). It has characteristics typical of longer compositions from the ancient Near East; representing these would enable the comparison between this case study example and other compositions that do not share any motifs or protagonists nor location, but instead are similar at a structural level. It also bears similarities to another text, The Old Man and the Young Girl (Alster, 1991–1993; Gadotti, 2014), providing an ideal opportunity for comparative analysis between representations of texts that have similar content and context. Finally, consisting of incomplete fragments separated by several centuries, the primary sources that bring us this story are a tangible example of unknown, incomplete, and ambiguous unstructured Humanities data.26 4.6.2

Three Ox-drivers of Adab

The first 30 lines of the inscription set the scene for an ancient joke or riddle (Alster, 1991–1993; Foster, 1974; Lambert, 1995). This translation is as provided by the Electronic Text Corpus of Sumerian Literature:27 Our king! We are ox-drivers. The ox belongs to one man, the cow belongs to one man, and the wagon belongs to one man. We became thirsty and had no water. We said to the owner of the ox, “If you were to fetch some water, then we could drink!”. And he said, “What if my ox is devoured by a lion? I will not leave my ox!”. We said to the owner of the cow, “If you were to fetch some water, then we could drink!”. And he said, “What if my cow went off into the desert? I will not leave my cow!”. We said to the owner of the wagon, “If you were to fetch some water, then we could drink!”. And he said, “What if the load were removed from my wagon? I will not leave my wagon!”. “Come on, let’s all go! Come on, and let’s return together!” “First the ox, although tied with a leash (?), mounted the cow, and then she dropped her young, and the calf started to chew up (?) the wagon’s load. Who does this calf belong to? Who can take the calf?”

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The king is unable to provide a solution, and seeks in turn the council of the “cloistered lady”28 to whom he repeats the tale verbatim. She is able to provide a solution, but it is at this point that both tablets are damaged, and the modern audience is left without closure. 4.6.3 Ontological Representation

Three existing ontologies were combined to capture this data, which contained some that was ancient, and some that was modern: the CIDOC Conceptual Reference Model (CIDOC CRM),29 a rather well-known ontology for cultural heritage data and object biography; FRBRoo,30 a formal ontology for the representation of bibliographic information, specifically designed to merge with the CIDOC CRM and to thus facilitate the integration of museum and library data; and OntoMedia, the bespoke model that focuses on the representation of the narrative in multimedia and has similarly been designed as linkable to the CIDOC CRM.31 This approach resulted in an underlying interconnected graph structure, which could be used to capture the content of myriad other ancient inscriptions. If such a Linked Data project were created, it would enable queries that combine aspects of several different types of data: the narrative content of the text (ancient data, incomplete and at times ambiguous, consistently unstructured), the metadata of the website that carries it (structured data, if not fully standardised), the associated bibliography of secondary sources (standardised bibliographic metadata), and the museological data relating to the witness tablets as physical objects in two separate collections in cultural heritage institutions residing on different sides of the planet (similar, but with minor institutional differences). An example of such a question could be: Show me all the inscriptions that are from the Old Babylonian period from the city of Nippur, and no larger than 15 cm in size, which have been acquisitioned into collections in the USA, have been on loan to an exhibition in the UK, and contain a narrative element of a woman giving a man advice. that has been mentioned in two or more publications. Such a query would combine elements uniquely captured by each of the three ontologies. 4.6.3.1

Material Objects in Museums

Let us consider the rather more straightforward, tangible aspects of this heterogeneous data. The physical characteristics (object measurements) of the items that carry the cuneiform inscriptions (the clay tablets, pegs, cylinders, stamps, and so on) map to the existing Classes and properties of the CIDOC CRM with relative ease.32 In terms of objective truth, there is scope here for detailed data provenance: the properties enable the user to specify which type of physical object was used to measure the ancient one.33 In summary, the physical characteristics that

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are captured in the object metadata categories, being of more recent origin than the inscription itself, are a clear fit for the CIDOC CRM. But let’s push this into a more complicated space. The CIDOC CRM is an eventbased model. This means that, rather than a direct correlation between two things, they are connected through an event. For example, a mother and a child would be connected through the event of the birth. The crucial detail here is that the events in question are not ones from the narrative of the story. These are the remit of Ontomedia; for CIDOC CRM, the events related to the cuneiform object would be the moment it was created by the ancient scribe, or the moment it was acquisitioned into a museum collection. Alas, the represented data was restricted to information which is available via Electronic Text Corpus of Sumerian Literature34 exclusively, and it does not contain information about either of those events. As well as being event-based, the CIDOC CRM is also a top-down or universal ontology. With this comes a greater degree of abstraction in the categorisation data, as each Class strives to be non-specific enough to be relevant to several different types of instance. Each text, for example, can also be mapped as an instance of E73 Information Object, which is conceptually separate from the physical tablet (E84 Information carrier) that it is written on. The measurements are of the physical object, not of the text. Furthermore, as a nested subgroup of E24 Physical Man-Made object, E84 inherits the connection between E24 and E90 Symbolic Object, offering an alternative way for representing the relationship between physical object and the text it carries. The benefit of this abstract approach of connecting the physical object and the text (which nevertheless are seen as separate entities) is that attributes about each can also be easily declared. For example, the maker of the tablet might be a different person to the composer of the text (imagine a scenario where a pre-shaped tablet is handed over to the scribe who proceeds to write something on it), or there may be things about the tablet biography that are separate from that particular inscription, as would be with the case of tablets where an initial inscription has been erased and another written over the top of it: the object would be the same, its measurements would be the same, but the text it carried on its surface would have changed to a completely different one. The full representation of the object biography would thus include two separate but equally valid assertions about the text that the object carried. The only way to specify a sequence or differentiate them in time would be through the addition of often contextual information (e.g. a relationship in time using properties such as P173 starts before or with the end of (ends after or with the start of) and the Class E2 Temporal Entity. The process of mapping the concept of the text to the CIDOC CRM was relatively straightforward as it was limited to a generic abstraction: it is an instance of F23 Expression Fragment, as well as that of E73 Information Object. This means it can be shown to have a language and the bidirectional relationship between translation and transliteration can be represented through the use of E33 Linguistic Object, E56 Language and P72 has language.35 At this level of complexity, we could facilitate a query that would, for example, ask the system to show tablets that were of a particular size, and carried

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an inscription in a specified language. The query is simple at this stage because only one type of information has been captured in the ontology so far. 4.6.3.2 Bibliographical Metadata

To increase the complexity of the underlying ontological structure, and to thus facilitate a more complex query, we can add additional information about the ancient inscription. For example, the bibliographic data of secondary publications capturing six categories of information can be mapped using LRMoo.36 In the context of the case study of representing Sumerian literature, the ontology of choice was FRBRoo,37 which was connected to CIDOC CRM through the aforementioned Classes of E73 Information Object, of which F2 Expression is a subclass. FRBRoo has since been replaced by LRMoo, but the Classes and properties used in the project were from FRBRoo. These are described here. Modern scholars named in the bibliography can be mapped as instances of F10 Person, which is an equivalent of E21 in the CIDOC CRM: FRBRoo also includes F38 Character, which is defined as referring to “fictional and iconographic individuals . . . appearing in works in a way relevant as subjects. Characters may be purely fictitious or based on real persons”.38 The abstraction here is key to its applicability to a wide spectrum of protagonists, it is equally valid for the unnamed characters (the slave-girl, the young scribe, the ox-driver) as it is to Gilgameš. Interestingly, doing so is in stark contrast to the classifications of modern narratologist, such as Propp and Campbell, who differentiate between the hero and the villain. The problem with their approach is that stories can have much greater narrative complexity than what this categorisation represents. Those complexities can include changes as part of character development: Gilgameš is an excellent example of this. Arguably the most well-known of Mesopotamian literary heroes, he is, depending on the composition, perspective of the audience, and section with a piece, equally cast as either hero or villain, perhaps simultaneously both and thus by logical deduction neither. A semi-mythical demigod possibly originally based around the personal cult of a real person, the story of the Epic of Gilgameš (for a translation, see George, 1999) is (at least partly) one of redemption through personal growth: at the beginning, he is a monster, a tyrant, a rapist (exercising jus primae noctis) whom his people beg the gods to destroy – at the end, he is the champion of mortality, a forlorn lover perhaps. How then are we to categorise him? Abstraction and lack of value judgement based on modern-day perspectives and loaded terminologies (a hero being good, a villain being bad) makes F38 the ideal Class for representing even protagonists as complicated as Gilgameš. 4.6.3.3 Narrative Structure

The content of the inscription cannot be mapped to either CIDOC CRM nor FRBRoo, but the OntoMedia ontology has several relevant Classes and properties. The three men (Man1, Man2 and Man3) are all represented as om:Characters who are Gender:Male.39 Each also has an

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om:Possession: for Man1 it is an instance of om:Ox; for Man2 an entity in the class om:Cow; for Man3, it is a Physical:Item (string:“wagon”). It is even possible to capture the giš determinative of the Sumerian (gišmar), showing this physical item is made of om:Material (string: “wood”). Each has a Profession:Rural, which is sufficiently abstract to avoid any modern connotations of divisions into specific agricultural roles. The men are connected to each other via an Alliance:Friendship. They also have an om:Alliance to the king, as well as being citizens of an om:City (Adab). The men together are an instance of an om:Group. The king is also an instance of om:Character with Gender:Male. He has a Profession:Ruler and has an om:Alliance to Adab. The cloistered lady is an om:Character with Gender:Female. This adherence to the gender binary reflects neither an exclusively modern categorisation nor a dismissal of a potentially more diverse gender spectrum of antiquity, but reflects a very literal description of the men (with the word lu2, and for the king, lugal) and of the “cloistered woman”.40 The interactions between instances of the om:Character are represented through a series of om:Events, which occur across a number of om:Timelines in two separate om:Contexts. These include Gaining Bond:Enmity for each other (the quarrel); om:Travel between two unknown om:Locations; om:Ox engaging in Action:Sex with the om:Cow; the om:Bovine (young)engages in om:Consume of the wagon’s load, which is a Physical:item. Almost all of the different elements of the story can thus be mapped individually to the OntoMedia ontology, proving the universal nature of that upper-level ontology. It shows that it is possible to map all kinds of fictional narratives. It also speaks significant volumes as to the universal nature of storytelling, and thus of shared human experience across cultural boundaries. There is but little in terms of differences and diversity in the ways members of the human species experience and understand the world. Even when separated by thousands of millennia and miles, the topics and motifs that occur in stories are universal. What this mapping shows is that abstract concepts can be defined in ways that ensure that moral, ethical, and social judgements are excluded from the process of ontological modelling. Where the OntoMedia ontology fails to adequately capture the narrative elements of the ancient inscription is in the granularity of mapping different types of dialogue. This is a fundamental part of the tradition of the so-called Wisdom Literature genre in Sumerian literature; it consists of dialogues, diatribes, debate poems, proverb collections, and so forth. Given this is an entire genre of literature, a specialist ontology would be needed to adequately capture the taxonomy of types of dialogue. There are two potential challenges to developing such an ontology: first, it is possible that the categories might be too specific to Sumerian literary categories, and the model would not be of use to any other types of compositions, potentially resulting in information siloing when no other datasets in RDF share the same Classes or properties; second, and much more importantly in terms of this chapter in particular, is that in doing so we might be inadvertently perpetuating

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modern categorisations of ancient literature, which would have seemed alien and nonsensical to the ancient scribes writing them. But is this a problem of technology? Not exclusively! It is a challenge presented by computational tools. In other words, it is a challenge of the Humanities, both traditional and Digital. In fact, Ebeling (2007: ff33) has already noted this exact problem, saying the classification of literary compositions is a modern convention, and there are but a few indicators that would support any theory that the people of ancient Mesopotamia thought of the varied collections of mythological and historical narratives, royal praise poetry, hymns, and proverbs as one cohesive unit. Even drawing a defining line between literary (fiction-based) and historical (fact-based) pieces is complicated – examples of the latter (written regarding people who are known to have existed) can contain seemingly fictional elements; consider, for example, Šulgi (a genuine historical person, and a king of the Ur III dynasty) claiming Gilgameš as his brother (Taylor, 2013: 301). Another similar example is the role of the ox-drivers. In this inscription, the three men are described exclusively in terms of the things they possess: one owns an ox (gud lu2 diš-a-kam), the other a cow (ab2 lu2 diš-a-kam), the third a wooden cart (jišmar lu2 diš-a-kam).41 But could we refer unambiguously to these men as agriculturists, or farmers? Not necessarily, as doing so would risk making inherent assumptions about the division of roles and labels to members of bygone societies and civilisations for which we have insufficient evidence to either confirm or deny. And perhaps it is here that it is a good time to stop and think about just how deep-routed biases are. If we have to be reminded of different ways of understanding personhood, what else might we take for granted in database and information representation designs? 4.7

Conclusion

The information structures and computational technologies developed in Silicon Valley have been criticised for their lack of consideration of cultural, linguistic, ethnic, and economic diversity. As driving forces behind the development, the oligopolies of computational technology have affected information representation in modern society. Reductionist technologies have sought to eliminate diversity and ambiguity (rather than capture it) for the sake of quick and efficient development, and have failed to adequately represent the global population. Tasks as seemingly simple as adequately and appropriately, as well as respectfully representing the name of an individual are complicated or even prevented by database structures. Western perspectives have permeated almost every aspect of information capture, representation, and analysis. From Linnaean taxonomy to Google search results, the Western ways of categorising the world have become the only acceptable voice of authority. The case study data, which is the narrative content of an ancient inscription (the fabula as the events of the story in chronological order and the syuzhet as the actual employment of the narrative, which included narrative devices such as focalised analepsis or flashbacks, as told from the perspective of a protagonist in the story),

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is an example of unstructured data. The broken witness tablets42 are tangible and physical proof of this being a case of incomplete data, and the uncertainties of the translation of the words in the text a manifestation of ambiguity and uncertainty in and about the data. Having established the basics of the Linked Data publication paradigm in the context of the Digital Humanities (Chapter 1), and given consideration to the ways in which issues of privacy should be taken into account when applying this methodology (Chapter 2), and with the wisdom of a case study example of the effects of institutional policy (Chapter 3), attention turned to the consideration of how social, personal, and cultural perspectives can affect the implementation of Linked Data. In the next two chapters, discussion will focus on the way inherent biases (such as the moral and ethical values of modern society) can and do manifest in the development of ontologies and thus the implementation of the Linked Data paradigm as a whole. In the context of the Semantic Web and the Linked Data publication paradigm, ontologies are less to do with the philosophical categorisation and representation of truth, and more to do with the explicit articulation, in a machine-readable format, of the data entities and the relationships between them that occur in a given dataset. But how do we decide what constitutes a data entity, and how do we know which relationships to map? This is all part of a lengthy design decision process, one which is often quite poorly documented. The way the Universe and reality are perceived can and do influence ontology development. The risk of presentism as it is understood in historical analysis on knowledge representation was the focus of this chapter. Discussion has centred on the risk of introducing anachronistic present day values, knowledge, ideas, and perspectives into the capturing of what is intended to be timeless truths and facts about a dataset. The concept is not pushed all the way to philosophical presentism, which is the view that neither the future nor the past exists, but to the recognition that the ontological representation of data in the Linked Data paradigm is one of a snap-shot in time. We can illustrate these ideas in a clear way by opting to focus on the challenges of applying modern technologies to historical and ancient world data. The software and informational representation tools available today are created with the expectation that they will be used to complete datasets, created from an omniscient perspective. Furthermore, they are designed to represent the modern day understanding of the world. This chapter posed the question: “How well do these modern technologies lend themselves to unbiased examinations and representations of the past?” Perhaps the right question to ask should have been: “How well do these modern scholars lend themselves to unbiased examinations and representations of the past?” Notes 1 I refer to the term “meme” specifically and exclusively as it is understood in Internet culture, as a concept or repurposed image that is spread from one user to another across social media platforms, email, and other content. 2 https://paulspurpose.com/2017/11/08/69/. Accessed 07/04/2022.

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3 www.cidoc-crm.org/. Accessed 27/05/2022. 4 https://cidoc-crm.org/Resources/crmdigital-v3.2-an-extension-of-cidoc-crm-tosupport-provenance-metadata. Accessed 30/01/2023. 5 https://cidoc-crm.org/crmsoc/Resources/crmsoc-a-cidoc-crm-extension-for-socialphenomena. Accessed 30/01/2023. 6 https://linked.art/model/profile/. Accessed 26/01/2023. 7 Some of the big players in the current Tech-Meets-Data-Economy landscape do reflect a more diverse demography (Hewlett Packard with CEO Enrique Lores; Alphabet, and its subsidiary Google, with Sundar Pichai; and Shantanu Narayen at Adobe Systems). 8 By “beauty” I don’t just mean an aesthetic value of a thing or person, but the criteria that is applied to success. Their ideas for the correct thing, the right thing, the right and correct way of categorising the world. 9 www.theguardian.com/commentisfree/2021/apr/03/why-silicon-valley-most-astutecritics-women-tech-gender. Accessed 15/06/2021. 10 www.cidoc-crm.org/. Accessed 11/06/2021. 11 https://cidoc-crm.org/frbroo/ModelVersion/lrmoo-f.k.a.-frbroo-v.0.6. Accessed 30/01/2023. 12 http://musicontology.com/. Accessed 11/06/2021. 13 http://motools.sourceforge.net/event/event.html. Accessed 11/06/2021. 14 www.w3.org/TR/prov-o/. Accessed 11/06/2021. 15 www.w3.org/TR/rdf-schema/. Accessed 11/06/2021. 16 www.w3.org/OWL/. Accessed 11/06/2021. 17 https://id.loc.gov/ontologies/madsrdf/v1.html. Accessed 11/06/2021. 18 https://id.loc.gov/ontologies/bibframe.html. Accessed 11/06/2021. 19 www.semanticdesktop.org/ontologies/2007/03/22/nco/#Gender. Accessed 11/06/2021. 20 http://xmlns.com/foaf/spec/. Accessed 11/06/2021. 21 http://xmlns.com/foaf/spec/#term_gender. Accessed 11/06/2021. 22 https://homosaurus.org/. Accessed 11/06/2021. 23 The collection of written works that are classified as “Sumerian literature” (a term where both words could justify a discussion) consist of an eclectic mix of compositions written for myriad socio-political and cultural reasons. They share a number of characteristics, such as a narrative arc, or the capture of a verbal exchange between two or more entities. These texts undeniably have some non-pragmatic purpose – they are not lists, nor glossaries, nor recordings of units of agricultural produce, but rather the product of creative and artistic minds. Furthermore, they are classified by Black and Zólyomi (2007: 3) as consisting of exemplar texts which were, in antiquity, produced (and survive) in multiple versions – Ebeling (2007: 33) agrees, elaborating on their universal nature. Many of the compositions in the online resource the Electronic Text Corpus of Sumerian Literature, and are also listed in ancient catalogues (Ebeling, 2007: 34). These texts played an important role in scribal education, not only as a vehicle for learning the cuneiform script, but also to teach the moral, cultural, historical and social values of their society (Taylor, 2013). 24 These texts are thought to be composed by scribes whose native tongue was Akkadian, and so “Sumerian” can only be applied confidently as a linguistic label, with no implication as to the cultural or social identity of the authoring scribe or the commissioner of the text (Brisch, 2013). 25 The composition (which, like most of ancient Mesopotamian compositions is named after the first line rather than by some other, more abstract or protagonist-driven name) has a total length of 95 lines, but it is only the first third that survives to any considerable extent, with the majority of content missing from line 30 onwards, and the final third of the composition is riddled with gaps. 26 Only two tablets carrying the text are known. There appears to be no known provenance for either tablet – the secondary authors have made little comment on possible

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31 32 33 34 35

36 37 38 39 40 41 42

“Truth” and Bias geographical origin or find spot for either witness, except to say none is known for the Louvre fragments (Alster, 1991–1993: 27). All of these features greatly complicate the process of unambiguously and explicitly mapping all the relevant data to an ontological structure. https://etcsl.orinst.ox.ac.uk/section5/tr565.htm. Accessed 09/06/2021. Akkadian “sekrum”, a somewhat ambiguous term for a female of the royal household who may or may not have had a cult or religious role. www.cidoc-crm.org/version/version-7.1.1. Accessed 09/06/2021. http://new.cidoc-crm.org/frbroo/ModelVersion/frbroo-v.-2.4. Accessed 10/06/2021. February 2020 saw the replacement of FRBRoo with LRMoo (https://cidoc-crm.org/ frbroo/ModelVersion/lrmoo-f.k.a.-frbroo-v.0.6. Accessed 30/01/2023), but the ontology used in this project was the FRBRoo. www.semanticscholar.org/paper/OntoMedia%3A-An-Ontology-for-the-Representation-of-Jewell-Lawrence/c8e6add16ad6efd76ca13135c5fcf808f73cf3c5. Accessed 24/01/2023. These include the objects measurements E16 Measurement and P39 measured (was measured by) and P40 observed dimension (was observed in). How often this information is provided in a collections database, well, I remain sceptical, but the point is that ontologically speaking, the representation is possible. https://etcsl.orinst.ox.ac.uk/index1.htm. Accessed 09/06/2021. The concepts here are sufficiently abstract not to cause a risk of presentivist interpretations. We know that the concept of language, for example, was one that was clearly attested in Sumerian records (eme-gir). Similarly, abstract nouns and concepts were clearly part of the Sumerian world view, as attested by the use of the prefix -nam, which turns concrete nouns into abstract ones. https://cidoc-crm.org/frbroo/ModelVersion/lrmoo-f.k.a.-frbroo-v.0.6. Accessed 30/01/2023. www.cidoc-crm.org/frbroo/home-0. Accessed 24/08/2021. On p. 65 of the scope notes for the FRBRoo, available at http://new.cidoc-crm.org/ frbroo/sites/default/files/FRBRoo_V2.4.pdf. Accessed 10/06/2021. Although OntoMedia would allow for a more detailed specification of om:Sex (Behavioural, Genetic, Gonadal and Phenotypic), since these details are not clear, nor provable from the context of the story, the differentiation is not made here. Translated in the Electronic Pennsylvania Sumerian Dictionary as a term referring exclusively to women. See http://psd.museum.upenn.edu/nepsd-frame.html. Accessed 10/06/2021. Available at https://etcsl.orinst.ox.ac.uk/section5/c565.htm. Accessed 09/06/2021. The translation is available at https://etcsl.orinst.ox.ac.uk/section5/tr565.htm. Accessed 09/06/2021. This example is one that also strongly supports the need to engage with primary source material in the original language, rather than through translation – a point of debate amongst some in the Digital Humanities.

Bibliography Allemang, D., and Hendler, J. (2011). Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL. Elsevier. Alster, B. (1991–93). “The Three Ox-Drivers from Adab”. Journal of Cuneiform Studies, 43–45, pp. 27–38. Black, J. and Zólyomi, G (2007) “Introduction to the study of Sumerian”. In Ebeling, J. and Cunningham, G. (eds) Analysing Literary Sumerian Corpusbased Approaches, Equinox Publishing Ltd.

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Brewster, C., and O’Hara, K. (2004). “Knowledge Representation with Ontologies: The Present and Future”. IEEE Intelligent Systems, 19(1), pp. 72–81. Brisch, N. (2013). “History and chronology”. In Crawford, H. (ed). The Sumerian World, Routledge. Campbell, J. (2008). The Hero With a Thousand Faces (Vol. 17). New World Library. DuCharme, B. (2013). Learning SPARQL: Querying and Updating With SPARQL 1.1. O’Reilly Media, Inc. Ebeling, J. (2007). “Corpora, Corpus Linguistics and the Electronic Text Corpus of Sumerian Literature”. In Ebeling, J., and Cunningham, G. (eds). Analysing Literary Sumerian Corpus-based Approaches, 33–50. Equinox. Foster, B. R. (1974). “Humor and Cuneiform Literature”. Journal of Ancient Near Eastern Studies 6, pp. 69–85. Gadotti, A. (2014). “Sumerian Wisdom Literature”. In Chavalas, M. (ed). Women in the Ancient Near East. Routledge. Geertz, C. (1973). The Interpretation of Cultures. Basic Books, Inc. George, A. (1999). The Epic of Gilgamesh: The Babylonian Epic Poem and Other Texts in Akkadian and Sumerian. Penguin Books. Grant, K. (2022). Landscape and the Arts in Early Modern Italy. Amsterdam University Press. Gruber, T. R. (1993). “A Translation Approach to Portable Ontology Specifications”. Knowledge Acquisition, 5(2), pp. 199–220. Hyvönen, E. (2018). Semanttinen Web: Linkitetyn Avoimen Datan Käsikirja. Gaudeamus. Kranzberg, M. (1986). “Technology and History: ‘Kranzberg’s Laws’”. Technology and Culture, 27(3), pp. 544–560. Lambert, W. G. (1995). “A Note on the Three Ox-Drivers from Adab”. NABU, 2, pp. 1–2. Nurmikko-Fuller, T. (2014). “Assessing the Suitability of Existing OWL Ontologies for the Representation of Narrative Structures in Sumerian Literature”. Current Practice in Linked Open Data for the Ancient World, ISAW Papers, 7. http://dlib.nyu.edu/awdl/isaw/ isaw-papers/7/nurmikko-fuller/ Nurmikko-Fuller, T. (2018). “Publishing Sumerian Literature on the Semantic Web”. In Juloux, V., Gansell, A., and Di Ludovico, A. (eds). CyberResearch on the Ancient Near East and Neighboring Regions. Brill. Nurmikko-Fuller, T. (2022). “Teaching Linked Open Data Using Bibliographic Metadata”. Journal of Open Humanities Data, 8. Pratchett, T., and Gaiman, N. (1990). Good Omens: The Nice and Accurate Prophecies of Agnes Nutter, Witch: A Novel. Victor Gollancz Ltd. Propp, V. (1968). Morphology of the Folktale, 2nd ed. University of Texas Press. Taylor, J. (2013). “Administrators and Scholars: The First Scribes”. In Crawford, H. (ed). The Sumerian World. Routledge. Wilks, Y., and Brewster, C. (2009). Natural Language Processing as a Foundation of the Semantic Web. Now Publishers Inc. Witteveen, J., and Müller-Wille, S. (2020). “Of Elephants and Errors: Naming and Identity in Linnaean Taxonomy”. History and Philosophy of the Life Sciences, 42(4), 1–34.

5

5.1

Data Demands

Preamble

“The Man Who Mistook His Wife for a Hat” (Sacks, 1985) is a collection of patient stories compiled by a neurologist, each chapter telling the tale of one condition or another, and the ways in which it affected the patient. These cases include The Lost Mariner, who is incapable of forming new memories and indeed believes himself to be several decades younger than he is; and, The Disembodied Lady, a largely unique case of “a strapping young woman of twenty-seven, given to hockey and riding, self-assured, robust, in body and mind. She had two young children, and worked as a computer programmer at home” (ibid.: 26), who lost control of her body. The eponymous patient is a musical genius referred to as “Dr P”: he has lost the ability to identify things that he sees (although sounds he hears are obvious to him). His condition means that he cannot recognise people by their faces, and, instead, sees what his brain interprets as human faces in what turn out to be inanimate objects, such as fire hydrants, and furniture. And yes, he does, in fact, according to Sacks, at one time literally mistake his wife’s head for a hat. Although some of the analysis and hypotheses are dated (the Disembodied Lady is initially diagnosed/dismissed as being “hysterical”), these stories provide an insight into the complexities of the human brain. They show us, especially through the mistakes, something of how information is categorised in our brains. The discrepancy (the deviation from what we perceive as “normal” cognition) between reality as perceived by others (the hat) and the alternative assessment of the situation (looking at his wife but his brain processing that to be the hat) are almost comical, absurd, and satirical. But what is this, but a conflict between one way of understanding reality, and another? The parallels between the human brain and computation have in one form or another been part of research discourse around the various takes on the Mind-Body problem for over two millennia. The ideas of Plato and Aristotle (some 5th–4th centuries BCE) are predated by those in Buddhist thought from around 500 BC. In the West, the ideas of Descartes, Kant, Huxley, and Searle continue to influence our thinking. The history of artificial intelligence is littered with attempts to recreate human cognition as a process. We might look to researchers such as Miller, Chompsky, or Pinker, and mull over the ideas of learning about human cognition DOI: 10.4324/9781003197898-5

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through the process of computational modelling. But, what of a somewhat different approach? Not one of mimicking the process, but technology as contributing to it? Enter Clark and Chalmers (1998) and the Extended Mind thesis: to summarise its main elements, Clark and Chalmers put forth the idea that technology that exists outside of the human mind can be used to extend it, and to support cognitive processes such as memory. Their thought experiment is a comparison between Otto and Inga, both on their way to the museum. Inga can remember the route (the cognitive processes of determining the geospatial coordinates of the final stop and calculating the optimal route there all occur in her mind or in her brain). Otto, on the other hand, has Alzheimer’s and relies on a notepad for the same information. In his TED Talk in 2011,1 Chalmers declares that digital technologies such as iPhone (his specific example) have amplified the potential of technology to complement and even overtake cognitive processes. These devices now complete processes that the biological brain used to perform: tasks such as memory (phone numbers in Contacts), planning (iCalendar), spatial navigation (Google Maps), desires (via an app that retains favourite recipes in a restaurant), and even (although the point is made a bit facetiously) decision-making. The popularity of these computers also means that the thesis is now much more compelling to a larger number of people, who can readily identify with these examples. For the purposes of this chapter that focuses on information structuring and preservation, and this book, which looks at how Linked Data can be used to infer new knowledge, this rings particularly true in two different ways: firstly, databases as memory, and secondly, computational tools and methods for speeding up the retrieval of very specific data snippets (an information retrieval task) so that the uniquely human processes of thinking and interpretation can be utilised to their greatest effect. In other words, the Extended Mind thesis rings true because digital and computational tools are there to store information that we can later retrieve or access; filtering through results can help us pinpoint relevant data for analysis; but, at the core of it all is human cognition, doing something unique in terms of judgement and analysis. Given then that museums (and institutions of the other designations in the GLAM acronym)2 of different designations and locations (also referred to as “memory institutions”!) contain such different types of collections retaining very different kinds of information, is it even possible to develop a database that could faithfully represent the information held in all of them? And, is this a uniquely technology driven demand? If information was the sole responsibility of an individual, would one expect (or want) a fully unbiased account? Or, would one expect and even cherish the individual slant the information custodian would put on the knowledge that had been passed on to them by generations of predecessors? Would we not expect the human mind to reinterpret and reapply the knowledge of the past into a present situation, to make meaning of tradition and reinterpret that information in the context of the modern world? But, when it comes to databases, does the same flexibility apply? Our demands are surely the opposite, with expectations of a pristine preservation of all the information, in an immutable fashion. Is that not the ultimate evidence to suggest that we expect from our digital databases the objectivity of timeless omniscience?

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5.2 The More Things Change In April3 2022, whilst searching for something completely unrelated – a bureaucratic document, misplaced – I stumbled upon an email thread from 2011. This thread of no fewer than 68 emails (with an additional spin off thread of eight additional ones) was the Museums Computer Group mailing list, and had attempted to answer the question “What would an open source museum CMS look like?”. A fascinating, and seemingly timeless topic. A rudimentary text analysis highlighted a few key issues: the most frequently occurring words reflected a discussion further down the thread as to whether the acronym “CMS” referred to a “content management system” or a “collections management system” – how appropriate for this particular list! And yet, there is a note of irony here, given I’ve chosen to mention it in a chapter about knowledge representation: the criteria of a collection management system for a museum are significantly different from a content management system that sits behind a website. Beyond those obvious terms, the words that cropped up most frequently were “data”, “open”, “web”. What is less obvious from the topic modelling but clear in the thread is that at that point in time, a decade ago, the tech du jour was Drupal, with most early contributors to the thread discussing it and its relative pros and cons. WordPress is mentioned exactly the same number of times, but appears in the thread as a new and emerging option. Linked Data is mentioned a handful of times, each time as a new or even “future” technology. The debate between Drupal and WordPress is not what makes this thread an evergreen classic: it is old hat. But, from the beginning, there are a number of comments and observations from this Special Interest Community (SIG) that still remain a matter of debate: “As much as museums look similar, each believes itself to be unique” is surely an axiom of the GLAM sector; “[in museums] you have objects, interpretations and metadata”; and “all museums . . . have distinctive collections, brands and so on, but once you strip this away pretty much every museum website, large or small has the same core functions”. Perry Bonewell (Bolton Council, UK) makes a poignant comment: It is often not the system per se an institution uses but the way it is used that causes many of the problems; things like system/s implementation and development, basic and advanced documentation practice and a rudimentary understanding of the learnt skills involved in knowledge organisation, data quality control, user attitude and support, institutional attitude and resource etc. Many cultural institutions simply do not have . . . the resources to do something that is quite complex well enough to get the outcomes they need/demand. And so we encounter the absence of neutrality in technology, harking back to Kranzberg’s law4 from Chapter 4. The ways in which information is captured in these databases is a reflection of someone’s reality (or indeed that of many people), sometimes defined as being different from something similar by virtue of minutiae. Museums with very similar collections will insist on some unique aspect: we might say that it equates to an idea where one museum insists on being the keeper of hats,

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the other of caps, and both unilaterally reject the possibility of using a database that was designed to capture metadata for headwear. The challenges arise most predominantly if we are to propose a different categorisation altogether. It is difficult to come up with a meaningful example because this deviation from standard categorisation in information structuring comes across as absurd. If it was a person, we might call it madness. If there are museums where the collection is entirely based on a colour, for example, or on the tactile sensation of softness or the sense of something evaporating, they are surely minor and considered to be niche or quirky. And, even if we were to focus our thought experiment on a tangible object, museum databases are rarely designed to collect anything akin to memories of the emotional responses users had when the object was in use, or the things that we think of as being one or more degrees of separation away from the object itself. For example, the metadata record for a yellow teacup is unlikely to record that it was half of a pair that didn’t match in colour or shape or design but were forever designated “a pair” because they were gifted by a colleague at the same time. That is not to say that these characteristics couldn’t be used to create collections, nor that they don’t add to the rich biography of the object. And, as human beings we collect objects for their intangible and tacit qualities quite readily. Consider a collection of CDs or vinyl, for example, all from different artists but finely curated to suit the eclectic and specific tastes of one person. So if we can readily accept collecting different kinds of things, objects, items, why do we struggle with the idea of collecting different kinds of information about things? And what determines what information we do collect about things? And why do we categorise things the way that we do? Cultural and idiosyncratic understandings might well determine the categories in which we slot things in our understanding, but where (and when!) does the idea of systematically organising information in this way originate from? In our 2021 paper, Paul Pickering and I referred back to the thoughts of Vannevar Bush in his design of the Memex, but I provide the longer quote here as it clearly illustrates the connection to thinking about the GLAM sector in particular: Our ineptitude in getting at the record is largely caused by the artificiality of systems of indexing. When data of any sort are placed in storage, they are filed alphabetically or numerically, and information is found (when it is) by tracing it down from subclass to subclass . . . one has to have rules as to which path will locate it, and the rules are cumbersome. . . The human mind does not work that way. It operates by association. With one item in its grasp, it snaps instantly to the next that is suggested by the association of thoughts, in accordance with some intricate web of trails. Aren’t graph databases and triplestored of Linked Data a much better fit for some interconnected thinking? They have immense potential to be used for a much more faithful mapping of the cognitive processes of scholars into the digital realm. This idea is echoed strongly by Simpson (2020) “graph-based hypertext . . . provided a better way of representing the interconnected way in which I thought and worked”.5

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So let’s assume we’re now sold on the idea of Linked Data. Where next? What do we do, and where do we start? The truth is there are as many different workflows for Linked Data production, and everyone has their own favourite, based on what they know, what skills they have, and what approach best matches with their desired research outputs and agendas. Some tools work better with different data formats, and there is always a decision to be made as to where along the way is the most costly investment – that of human time and effort – to be made. All these considerations affect the recommendations we make. 5.3

Bias in Tool Recommendations

One of the most frequently asked recommendations is for software. “Which tool should I use?”, “What programming language should I learn?”, “What’s the best database?”. For a beginner, perhaps a student or academic looking to start their first project, these probably seem like entirely reasonable questions. The problem here is, they are in many ways unanswerable. Or, to put it another way, the only truthful and accurate response can only ever be the somewhat unspecific and undoubtedly frustrating “It depends”. Many if not most will make recommendations based on their experiences of both using and teaching the use of various software tools (I did so myself in Nurmikko-Fuller, 2022). The trick here is though that these recommendations are based entirely and subjectively on personal experience, which, in turn, has historically been influenced by several external factors, such as the parameters of prior projects that required the use of a particular tool. In some cases, projects may run snippets of code that were initially developed as an ad hoc solution to a specific problem within a limited time frame in some completely different context. The issue to be aware of here is that, once the time and effort has been invested into learning a specific tool or piece of software, the bar for reusing that tool is low, even if it is not the “best” tool, platform, or software out there. In situations where researchers experience additional demands on their time means they are less likely to be able to dedicate time to extensively evaluate new tools. It is easy to resort to using a tool or engaging in a workflow that is already familiar, as long as it is good enough even if it is not an ideal match with the project skills, data, or even the research aims. To return to the notion of “best” with regard to any equipment, tool, or methodology, the challenge here is that, no matter how useful that might be, there is no universal standard or criteria for assessing the inherent “betterness” of one tool over another. Would it not be best to avoid such hopelessly subjective terminology (the other great offenders are “easy” and “simple”, and for very much the same reasons)? The choice of tool, programming language, and digital infrastructure are all entirely dependent on factors such as the skills, ambitions, aims, and agenda of the researcher, as well the requirements of the data, and other external factors such as institutional policy (for example regarding Open Data), existing technical support at the institution (if any!), the budget, the size of the research cluster, and so on. The “best” tool in one context can be woefully inadequate in another; researchers

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prefer to invest human time at different points in the workflow; and finding an option that can be managed, used, and supported by the investigatory team is rarely a trivial task. To make the argument less abstract, we can consider the issue of databases. The division by Seth van Hooland and Ruben Verborgh (2014: 16) of databases into four categories is compelling and is applied here too: (i) tabular data, (ii) relational model, (iii) meta-MarkUp languages, and (iv) RDF (although much of what is said here could also apply to other graph databases). For a researcher wanting to start a new project, which approach should they take? The question may seem absurd in its reductiveness and simplicity – how could we possibly answer that without knowing more? But this is often the first step for novice researchers, and a genuine question. The issue here is that the question itself needs to be broken down to a greater level of granularity, and, we need to recognise two crucial aspects: first, that the process of creating digital projects is often reiterative, and may require the use of different tools at different stages; and second (and this is often an overlooked aspect), these tools are not in any way whatsoever mutually exclusive, and can and do often get used together. In the context of a Linked Data project, such as JazzCats (as discussed in the context of different data structures in Chapter 5) or the ElePHãT project (please see Chapter 3 for the description of this project and the effect of institutional policies), many of all of these information structures are used either concurrently, or as part of one cohesive workflow. One possible Linked Data workflow for the production of instance-level RDF that makes use of these technologies has been reported on elsewhere (Nurmikko-Fuller, 2022). The desire to pick the “best” tool is entirely reasonable (anything else would be baffling, a deliberate decision to opt for the suboptimal?), but the problem is that the question itself lacks subtlety and nuance. A more appropriate question might be along the lines of “What are the aspects of my project that should be considered when designing the workflow?”, and as a follow up “How can the tool or piece of software that is most suitable for each separate stage of the workflow be determined?”. The answer is determined by the answers to the long litany of considerations outlined earlier – each is a factor for determining the choice of tool. The reason this is not always given as the answer is often twofold: first, there is a fear that such an answer is unwelcome (we live in times of a surplus of easy answers and of instant gratification after all); second, the practitioner being consulted may feel that failure to name a specific tool implies a lack of expertise on their part – an irony, given the opposite is likely. There is a third option as well, which is that having invested considerable time, energy, and effort to master the use of one particular tool or method, we may be reluctant to consider the possibility that it is not the perfect hammer for every nail, or that we might not be the right person to include on a project or to approach for expert advice. The fourth alternative is closely related to this, but from the other side: Having spent considerable time, energy, and effort to master the use of one particular tool, we may be conditioned to see the world (or, at least research questions) from the perspective of that approach only.

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For many, when asked for recommendations for a specific piece of software, platform, programming language, or information storage structure, the answer is simple, but deceptive. Those with years and even decades of experience will assert, with absolute confidence, that they know the only possible technological solution to a(ny) given problem: Their Software of Expertise. In some cases, this might be a self-serving suggestion, allowing the person to position themselves as irreplaceable within a project; in others, it will be a well-meaning and genuine suggestion, but driven by a blinkered perspective. It does not immediately follow that the suggestions themselves are bad, inaccurate, or undesirable. What it does mean, is that they are the result of bias. It is the perspective of the expert whose answers reflect their lived experience, prior information and even personal choice. It is not to be seen as universal, objective truth. Information technologies actively shape the way we encounter information, and the way we interpret what we see. This is equally true of all information storage and structure approaches, be they library catalogues or the hidden knowledge structures of social media giants. Whether we are discussing the computational backend that enables Google’s microsecond searches that returns billions of hits, or the in-house, custom-built idiosyncratic relational database developed by a programmer for a specific project, or indeed a humble spreadsheet, the single copy of which sit on an individual researcher’s laptop, all of these approaches both are informed by and in turn inform the way that we process information, and the ways in which we categorise the world. All information technologies can be considered from two complementary but very different perspectives. On the one hand, there are socio-cultural and political considerations: these include aspects such as the popularity of a platform, the user experience, and the ease of initial uptake. These considerations – all human things – can have immense impact, but it should prompt us to ask questions about the phenomena. For example, is Facebook with its almost 2.8 billion users inherently better as a social media platform than Twitter, which has “only” 396 million users?6 And how would this be measured? What are the driving forces behind the choices of users when picking a particular tool, technology, or platform? On the other hand, we should consider the technological possibilities and limitations of any given digital platform or computational tool. To understand the technology, which this book focuses on – Linked Open Data – is to understand the role both these considerations have on affecting the way the method has been developed and used. Only a thoughtful, critical (but not necessarily exclusively negative) interdisciplinary evaluation of this methodology can enable us to systematically gauge the strengths and weaknesses of this technology. It is the process by which we can determine how we have come to know what we know (an information provenance trail, as it were) and to assess whether or not a given (undoubtedly unwavering and passionate) testimony of a methodological practitioner and researcher is based on personal preference or technological suitability. To be clear – as with any technology, Linked Data (nor Linked Open Data, a distinction that will become apparent momentarily) is not a silver bullet, and there will be times when it is not the most suitable tool. But, there will be other times, when the affordances and possibilities

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created by this information publication paradigm are revolutionary. This process of understanding the multifaceted nature of this technology is our way to ensure we are thinking about Linked Data critically and clearly, and asking not only the right questions but the right kinds of questions. Beer (2016) provides a scaffolding for his analysis of the history of Big Data, which lends itself, with minor tweaking, to the topic at hand as well. Beer takes his inspiration from Hacking’s 1991 publication (in a volume called The Foucault Effect) which discusses the need for a history of statistics, and applies these reflections to Big Data. Following these, the discussion here will cover the institutional, political, and other considerations of Linked Data, but rather than focus on the use of term by experts or the public (as Beer does), focus will be on the critical evaluation of the methodology. But critical is not intended to be exclusively negative – rather, each aspect of the discussion is viewed as a multifaceted thing, and assumptions as to whether any of those individual facets might be positive or negative are kept to a minimum. As such, the discussion must occasionally approach a topic from an abstract and almost philosophical standpoint – they will be balanced with examples and more tangible case studies. 5.4

Different Demands of Different Technologies

Different knowledge structures – be they relational databases, tabular data, unstructured text, MarkUp, a knowledge graph – all have various strengths and weaknesses. These broad categories of different types of structures are best understood through a process of explaining them alongside example processes of the conversion of data between different formats. To understand why a particular data format is more suitable in one context or another is achieved through comparison and contrast with others. Even a brief comparison between the Linked Data publication paradigm and other information structures shows that there are many parallels between it and relational databases (e.g. open-source relational database management system [MySQL]) and MarkUp languages (e.g. XML). The similarities between them include (i) the connection to Web technology (RDF uses Web technology, XML is closely linked to Web languages, and relational databases, whilst they do not naturally link to the Web, are in fact frequently used to store data behind websites); (ii) query languages (SPARQL and SQL share many similarities in function and syntax, and XML Path Language (Xpath)/Xquery/XSLT can similarly be used to extract information when working with XML documents); and (iii) several ways in which these technologies are brought together, for example, some triplestores use a relational database to store the RDF triples, there are tools to expose data from a relational database management system (RDBMS) as RDF, and, as discussed previously, one of the syntaxes for RDF is RDF/XML, and it is possible to annotate XML documents with RDF metadata. There are key technical differences. Graph databases differ from relational databases in a few fundamental ways. The nomenclature is the key here: the information held within the former is structured as a graph – it is not set in a structure or

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a shape, per se, the graph is decentralised and can be navigated from any starting point. In a relational database, data is structured in immutable tables, connected to other equally inflexible ones through key columns. The latter have had decades of investment of energy, time, expertise, and so on. The former are the computationally powerful way of capturing semantics and relationships between data entities. Triplestores, as the rather uninventive name will have given way, are graph databases that store RDF triples: purpose-built for the storage, editing, maintenance, retrieval, and so on of RDF triples in particular. Where Linked Data differs from both is that it is inherently flexible – any connections between nodes (properties between Classes) can be asserted, stored, and edited: in relational databases, data is categorised into predefined tables, eliminating or minimising any possibility of ad hoc changes. In the case MarkUp languages, the knowledge structure is directly dependent on XML language and must adhere to defined standard (such as TEI).7 Another difference is that where RDF necessitates the use of HTTP URIs, in relational databases the naming of columns is local. Again, in this regard, MarkUp languages are more similar, as XML namespaces make very similar use of URIs. One of the challenges of working with Linked Data is that in terms of development of software and tooling, the field is comparatively new; this is a considerable difference to both relational databases and MarkUp languages, for which there are numerous mature and scalable tools available. The biggest difference between Linked Data and the other two is in the way data is structured. The abstract data model of RDF results in a graph structure. As the nomenclature suggests, relational databases have data contained in sets or tables that (as is the case with MarkUp languages) usually map to hierarchical or tree structures. Each of the three (tabular data, relational databases, and Linked Data) have strengths and weaknesses, pros and cons. Although they are often described in that order, this listing does not reflect a linear progression of improvement. Each has a function and a purpose, and none of the three is always the right solution. For a comprehensive but easy-to-read comparison of these strengths and weaknesses, van Hooland and Verborgh’s (2014: ff 14) discussion is situated within examples of libraries, archives, and museums. An important consideration here is that various different tools and workflows can be utilised to create RDF from both tabular and relational data, enabling the aggregation of the information held in various different types of information structures using Linked Data. 5.6

Case Study: JazzCats

Linked Data is most productive when done in collaboration with others (Burrows and Nurmikko-Fuller, 2020). JazzCats (Jazz Collection of Aggregated Triples)8 is a Linked Data project for studying jazz artists, performances, and their prosopography through social and professional networks. The project combines metadata from three different sources – each of these consists of data held in a different format (tabular, relational, and RDF). As a case study example, it illustrates the

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different demands imposed upon the researchers and the workflow by these different data structures. The first iteration of JazzCats (Nurmikko-Fuller et al., 2017, 2018b) brought together three datasets: a discography of Body and Soul (that is to say, a catalogue of recordings of this particular jazz standard);9 the Weimar Jazz Database,10 which contains musicological metadata; and data from the Linked Jazz project, which is a prosopography of jazz musicians. More recently, RDF triples capturing information about jazz recordings from the Centre for Jazz Studies from Columbia University (J-DISC)11 were added to the underlying knowledge graph.12 5.6.1

Tabular Data: The Body and Soul Discography

The Body and Soul discography describes over 200 recordings, all dated between 1930 and 2004. It was originally conceived as supplementary material to Bowen’s 2015 article, and is available as a PDF online, but in neither a machine-processable nor non-proprietary format, and thus being an example of one star of the Five Star Linked Data Standard (described in Chapter 1). Although rich in relevant information, the data format (PDF) meant that it could not be incorporated into the project knowledge graph as is. An additional process of converting the content of this file was necessary in order to produce a structured dataset that could be used for subsequent stages of the project workflow. Personal correspondence with the author resulted in him sharing the original spreadsheet with us, and in using tools such as OpenRefine, the project team converted and tidied this data (Bangert, 2016). It is now available from the JazzCats project website as a CSV, and meets the three star criteria since it is published online, with an open licence, and in a non-proprietary format. Neither the content nor the structure of the dataset has changed. This example serves to illustrate that with relatively little effort, projects can publish their data at the Three Star Standard. The workflow for converting this tabular data into RDF has been documented elsewhere (Nurmikko-Fuller et al., 2018a) and discussed from a generalist Digital Humanities (Nurmikko-Fuller et al., 2017) and well as a musicology perspective (Nurmikko-Fuller et al., 2018b, October; Bangert et al., 2018, September). Here, it serves a purpose to repeat the simplicity of the workflow as an illustrative example and as a point of comparison with alternative methods deployed for the other datasets. The first stage of the production of instance-level RDF was to develop a datadriven ontological model. The ontology (which is extensively documented on the project website)13 was mapped where possible to the Music Ontology,14 and its two sister ontologies of Event15 and Timeline.16 The resulting structure consists of just eight Classes and 21 properties, but these are sufficient for the capture of the data held within the CSV file, and to enable us to add this information to the project knowledge graph. The ontology was developed and edited using the Stanford University free and open software Protege,17 well known in the field and used for ontology development across multiple disciplines.

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The resulting TTL file was uploaded alongside the CSV file to an instance of the Web Karma software, from the University of Southern California.18 A process of manual alignment was then carried out, using the point-and-click user interface (UI) of this tool, to specify which columns of the CSV contained the instances for a particular Class of the ontology. The resulting RDF triples are virtually errorfree, give or take a very small number of errors (fewer than five) that arose from unusual characters or spaces within the CSV that cause broken URIs to be minted from that data. The pay-off in terms of the investment of time is clear: the mapping process is time-consuming for the human researcher, but the resulting RDF triples are of high quality and require virtually no ad hoc editing. This is in stark contrast to the workflow for the production of RDF to capture the content of the Weimar Jazz Database. 5.6.2

Relational Databases to RDF: Weimar Jazz Database

Part of the Jazzomat Research Project, the Weimar Jazz Database,19 is an extensively curated collection of performance transcriptions (mostly solos) produced using Sonic Visualiser (Pfleiderer et al., 2017). The rich and diverse information held within the database contains details about performers and instruments, as well as metadata such as title, tempo, and key. The project data contains links to external authority files such as Wikipedia20 and MusicBrainz,21 which is an example of the Linked Data-readiness of the data, but also provides an opportunity for a workaround for copyright restrictions that prevent access to some of the data (such as contextual annotations): the use of temporal markers via MusicBrainz IDs enable the identification of existing solos, for example (Abeßer et al., 2014; NurmikkoFuller et al., 2018a). The Weimar Jazz Database sits in an instance of SQLite3.22 Web Karma, which is ideally suited for tabular data, was not the ideal tool for the conversion of this data into RDF. Instead, the project workflow (Nurmikko-Fuller et al., 2018a) consisted of using another open source tool, D2RQ.23 The benefit of this approach is that the process is largely automated; the drawback is that it requires a two-step process, where the second stage is a time-consuming one of data cleaning and management. The later stage involves the reconfiguration of the relational structure into a graph one, and thus also has a prerequisite need for the development of an ontological structure, or at least an awareness of the Classes and properties that the instance-level (relational database content) RDF should be mapped onto. And therein lies a point of fundamental importance. DHers and our supporting technical staff are often asked for advice regarding the “best” possible tool to complete a task. There is an existing culture of confidence (even hubris), and of uncritically promoting the tool that the person in the position to advise is familiar with using. But the correct answer here too is “It depends”. It depends on the desired project outcome, the aim of the specific task, the existing skills and knowhow of the person or people completing the workflow, and it depends on when the team wants to spend the human effort. A point to emphasise here is that the safest assumption is that there will be some point at which human endeavour plays a

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role, and that no workflow is free of it entirely. Where the research team has some choice though is in choosing the tool most suitable for the data structures that their data is natively in, and at what time of the workflow they choose to invest the most human effort. 5.6.3

Ready-Made RDF: Linked Jazz

The third different type of information category is RDF produced by a different project that was ingested into the JazzCats triplestore. In this case, the RDF was from Linked Jazz,24 a project from the Semantic Lab at Pratt.25 As with any aggregator project, there are two possible opportunities for combining the RDF from Linked Jazz with that of JazzCats: it could either be included via a federated query, or the RDF could be accessed, downloaded, and included into the JazzCats triplestore. There are pros and cons to either approach, and arguably more than one correct way of accomplishing either aim. The practical benefits of setting up a system that enables the user to query information from several databases using one point of entry are clear: this way, the user has access to aggregated information, but the responsibility for the maintenance and quality of the data are with the data owner. The challenges include security concerns and the need to establish the data mappings of what is essentially someone else’s database. The benefit to downloading and incorporating another project’s RDF into one’s own triplestore is that querying can happen locally. But, if the owner makes any changes to their data, the RDF incorporated into another triplestore will not be automatically updated, and thus risks deviating from the most up-to-date, gold standard quality of data as maintained by the data owner. 5.6.4

Querying Across Three Datasets Using SPARQL

For the JazzCats project, we converted tabular data into RDF (Body&Soul); translated a relational database into RDF (Weimar Jazz Database), and ingested a dataset that was already in RDF (Linked Jazz). We created a separate, named graph for each of the projects: that is to say, there were three distinct knowledge graphs in one triplestore. The benefit of this approach was that, if we so chose, we could query just one of the datasets by limiting the queries to that graph. Alternatively, we can query across all three graphs simultaneously. SPARQL contains a standard set of rules that determine the interactions between client programmes and a server. However, as DuCharme describes at the very first chance (2013: 1), “you can go far with the query language without worrying about the protocol, so this book doesn’t go into any detail about it”. And neither will this one. Ontologies and the Turtle syntax of RDF beautifully mirror SPARQL (the SPARQL RDF Protocol and RDF Query Language).26 Although not the most recent of publications, a comprehensive manual on the query language has been written by Bob DuCharme (2013). Other recommended sources include Wikidata’s SPARQL templates,27 and for anyone wishing jump in at the deep end, the SPARQL Playground28

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offers opportunities to learn ways to write increasingly complex queries to explore an underlying custom dataset. Anecdotally, many developers and programmers have commented on the similarity between SPARQL with regard to SQL, the (Structured) Query Language for relational databases. This undoubtedly reflects a history of education, whereby the latter is often incorporated into more traditional pathways for training coding, programming, and software development, and the former is the new kid on the block. Arguably there is scope for encountering SPARQL from a different perspective, one in which the relational middleman is skipped, and the graph-like thought processes of the Humanities scholar are directly translated into the query language that was developed specifically to query data published as RDF. Even the most devout Linked Data sceptic is won over when they get to try their hand at SPARQL – teaching the Linked Data workshop at the Digital Humanities Oxford Summer School for the last seven years has made this clear. Unfortunately for Linked Data, “while SPARQL has proven to be a powerful tool in the hands of experienced users, it remains difficult to fathom for lay users” (Ngonga Ngomo, 2013: 977) – and although there are the non-SPARQL frontends the likes of Pubby29 (for follow-your-nose-investigations) and SPARKLIS,30 no clear reigning champion of graphical user-interfaces (GUIs) enabling natural language questions equally across all Linked Data projects has yet emerged. the one produced by Nomisma.org31 is an excellent example of innovation geared towards providing services that are not just user-friendly, but represent some of the best approaches for enabling Humanities researchers to engage with Linked Data (without the need to learn SPARQL). The data management evaluation raises an interesting question around data provenance, privacy, and trust. The latter two are more comprehensively discussed in Chapter 2, but note that when we are (as we always are in Linked Data) in the business of amalgamating information from various different external sources, we must trust the originator of that data to provide us with the best quality data possible. Or do we? Digital Humanities projects that focus on a single dataset quite reasonably spend time and energy on data tidying. There is expectation and a correlation between the results and the responsibility of the project: if there is a mistake in the results, it is the fault of the project since they have provided inaccurate information. But what about Linked Data projects, where we pull in data from different sources? Should we now be responsible for the accuracy of all the data we have amassed? If yes, then how does that reconcile with the fact that any editing of the original data within the context of the Linked Data project has now resulted in a deviation from the gold standard data of the original project? What if the changes we made were in error, and new mistakes were introduced? Or what if the data is not edited, but it is misinterpreted or misused? 5.7

Conclusion

Different knowledge structures (relational databases, tabular data, unstructured text) all have various strengths and weaknesses. In this section, the broad categories

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of different types of knowledge structures have been outlined, and example processes for the conversion of data into a machine-processable format have been described. The JazzCats project has served as a case study example which combined jazz performance metadata from three different sources, each of which was held in a different kind of solution (a relational database, tabular data, and a graph database). But should we, as Linked Data practitioners, dedicate time and effort to understanding the history of index card systems, largely obsolete hypertext solutions, or the shift in public perception of all things computational being the domain of men when just some decades ago all “computers” were women? The answer is twofold. First, Linked Data, which is so fundamentally about connections and collaborations, is never going to deliver on its potential in isolation. To be able to collaborate with others, we must understand their approaches, and the challenges, opportunities, and strengths of the technologies and database solutions they use. Second, to be able to critically evaluate these technologies, we must understand the context in which they arose. All information representation systems from spreadsheets to triplestores are designed, built, and used to contain information. Not just contain it, but to manage, retrieve, and use data. Within these broad categories are myriad examples of specific implementations. Even if we focus on a specific context like the GLAM sector, there are vast numbers of different solutions. Some museums prefer custom-built databases, others opt for off-the-shelf solutions. The decision for which approach to take should be driven by the needs of the institution, the available information categories, the level of available resources, and the existing skills found in-house. Sometimes decisions as to which database to use are based on (expert) recommendations. The problem with recommendations is that they are always biased. We prefer solutions that we are already familiar with, or which play to our existing strengths. To think about it another way, would you recommend a tool that was difficult to use, and that seemed not to be fit for purpose? I would imagine not! Even wellintended recommendations can lead to suboptimal advice, as rarely are we fully familiar with all the considerations listed earlier with regard to someone else’s project. The same biases and reasons for these biases also apply to workflows. The workflows that worked for us in the JazzCats project are listed in this Chapter, but these were solutions developed for a particular set of circumstances, research agendas, and existing levels of skill. Reporting on these workflows serves a twofold purpose: first, they do offer a potential set of solutions that might help with the initial set up of a Linked Data project. Second, they illustrate that no matter what the original format of the data, it is possible to produce RDF with relatively “simple” processes. Many conversations in the Digital Humanities around Linked Data are centred around ideas of ease or simplicity, but this is a loaded set of questions. Software and tooling that has a low bar to engagement (ones that are intuitive to use, for example) are often recommended, but that rarely equates to simplicity of the tool – quite the

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opposite. In many cases, the simplicity of the user experience hides a remarkable level of complexity. Ultimately, each project plan and team has to make one decision: when to invest human effort. In the case of the workflows used in JazzCats, there are two clear examples of extremes. The Body&Soul workflow produced very high-quality RDF triples that required virtually no post hoc fixing or editing. The cost of this was that considerable human time and effort was used to map the data (at schema-level) and to produce the instance-level RDF in a largely manual mapping process. The opposite was true for the Weimar Jazz Database. The workflow was largely automated and as such, required little in terms of initial investment of human effort. The drawback of this is that the process produced a large number of superfluous URIs in RDF triples, including capturing the relational database structure. A series of SPARQL queries had to be run to edit these unnecessary triples from the graph – a largely human-driven task. In the case of Linked Jazz, decision-making was driven based on existing skillsets within the project team. The decision to ingest the RDF triples of another project meant that even with limited time and resources, we could maintain a triplestore with data from three distinct projects. We were more confident in maintaining one triplestore than we were establishing and maintaining options for federated querying. The drawback is that if Linked Jazz ever changes their triples, our knowledge graph will not be automatically updated. JazzCats is an opportunity to illustrate one additional point. Neither the owners of Body&Soul nor the Weimar Jazz Database had to alter their data, edit their informational storage structures, or relinquish control of their own dataset. In creating the RDF, we did essentially duplicate the same information, but the respective data owners were able to maintain their set ups unaltered. It is difficult to guess whether greater numbers of DHers will engage with the Linked Data approach in the future, but there are several benefits to doing so. Risk is minimised by maintaining a copy of the data in the original tabular or relational database; workflows can be picked to limit the need for extensive upskilling; and, projects can accurately and truly represent their knowledge though processes such as designing and implementing a data-driven ontological structure. The more projects and people to join in with Linked Data, and contribute to the Linked Data crowd, the more diverse and valuable the Cloud becomes. The bar has never been lower to take those steps towards accomplishing the techno-utopian dream of a Web that bridges the entirety of human knowledge. Notes 1 The talk is no longer available from the TED.com site, but it can be accessed on the TED. com’s YouTube channel, at www.youtube.com/watch?v=ksasPjrYFTg&ab_channel= TEDxTalks. Accessed 03/05/2022. 2 GLAM refers to “galleries, libraries, archives, and museums”. 3 As an example of culture-specific classification, I want to call this time of year “Spring” but, in Australia, it was autumn. 4 Kranzberg’s (1986: 545) First Law: “Technology is neither good nor bad; nor is it neutral”. 5 This too is a quote that appears in the Nurmikko-Fuller and Pickering (2021) paper.

Data Demands 101 6 www.statista.com/statistics/272014/global-social-networks-ranked-by-number-ofusers/. Accessed 23/07/2021. 7 TEI is the Text Encoding Initiative: https://tei-c.org/. Accessed 14/07/2021. 8 http://jazzcats.cdhr.anu.edu.au/. Accessed 22/06/2021. 9 Jazz standards are widely known, performed, and recorded compositions that often form the part of the repertoire for jazz musicians. 10 Weimar Jazz Database is available from https://jazzomat.hfm-weimar.de/dbformat/ dbcontent.html. Accessed 08/07/2021. 11 https://jdisc.columbia.edu/. Accessed 09/07/2021. 12 For more information about the datasets, see http://jazzcats.cdhr.anu.edu.au/about/. Accessed 09/0/2021. 13 http://jazzcats.cdhr.anu.edu.au/documentation/. Accessed 08/07/2021. 14 http://musicontology.com/. Accessed 08/07/2021. 15 http://motools.sourceforge.net/event/event.html. Accessed 08/07/2021. 16 http://motools.sourceforge.net/timeline/timeline.html. Accessed 08/07/2021. 17 https://protegewiki.stanford.edu/wiki/WebProtege. Accessed 08/07/2021. 18 https://usc-isi-i2.github.io/karma/. Accessed 08/07/2021. 19 https://jazzomat.hfm-weimar.de/dbformat/dbcontent.html. Accessed 09/07/2021. 20 www.wikipedia.org/. Accessed 08/07/2021. 21 https://musicbrainz.org/. Accessed 08/07/2021. 22 For more information about the database format, see https://jazzomat.hfm-weimar.de/ dbformat/dbformat.html. Accessed 08/07/2021. 23 See both http://d2rq.org/d2r-server and http://d2rq.org/ for more information. Accessed 09/07/2021. 24 https://linkedjazz.org/. Accessed 09/07/2021. 25 For more information see https://semlab.io/. Accessed 08/07/2021. 26 It is pronounced “sparkle”, and yes, it is a recursive acronym. 27 https://query.wikidata.org/. Accessed 26/01/2023. 28 https://sparql-playground.sib.swiss/. Accessed 26/01/2023. 29 www.w3.org/2001/sw/wiki/Pubby. Accessed 27/05/2022. 30 https://wiki.dbpedia.org/projects/sparklis. Accessed 27/05/2022. 31 https://numismatics.org/ocre/. Accessed 26/01/2023.

Bibliography Abeßer, J., Cano, E., Frieler, K., and Pfleiderer, M. (2014). “Dynamics in Jazz Improvisation – Score-Informed Estimation and Contextual Analysis of Tone Intensities in Trumpet and Saxophone Solos”. Proceedings of the 9th Conference in Interdisciplinary Musicology (CIM), Berlin, Germany, 4–6 December. Bangert, D. (2016). “JazzCats Body&Soul Discography [dataset]”. Zenodo. http://doi.org/ 10.5281/zenodo.163886. Bangert, D., Nurmikko-Fuller, T., Downie, S., and Hao, Y. (2018). “JazzCats: Navigating an RDF Triplestore of Integrated Performance Metadata”. Proceedings of the 3rd International Workshop on Digital Libraries for Musicology (DLfM), Satellite Event of the 19th International Society for Music Information Retrieval Conference (ISMIR), Paris, France, 28 September. Beer, D. (2016). “How Should We Do the History of Big Data?”. Big Data & Society, 3(1). Bowen, J. A. (2015). “Who Plays the Tune in ‘Body and Soul’? A Performance History Using Recorded Sources”. Journal of the Society of American Music, 9(3), pp. 259–292. Burrows, S., and Nurmikko-Fuller, T. (2020). “Charting Cultural History Through Historical Bibliometric Research: Methods; Concepts; Challenges; Results”. In Routledge International Handbook of Research Methods in Digital Humanities. Routledge.

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Clark, A., and Chalmers, D. (1998). “The Extended Mind”. Analysis, 58(1), pp. 7–19. DuCharme, B. (2013). Learning SPARQL: Querying and Updating With SPARQL 1.1. O’Reilly Media, Inc. Kranzberg, M. (1986). “Technology and History: ‘Kranzberg’s Laws’”. Technology and Culture, 27(3), pp. 544–560. Ngonga Ngomo, A., Bühmann, L., Unger, C., Lehmann, J., and Gerber, D. (2013). “Sorry, I Don’t Speak SPARQL: Translating SPARQL Queries Into Natural Language”. Proceedings of the 22nd International Conference on World Wide Web, ACM. Nurmikko-Fuller, T. (2022). “Teaching Linked Open Data Using Bibliographic Metadata”. Journal of Open Humanities Data, 8. Nurmikko-Fuller, T., Bangert, D., and Abdul-Rahman, A. (2017). “All the Things You Are: Accessing an Enriched Musicological Prosopography Through JazzCats”. Proceedings of the International Digital Humanities Conference 2017 (DH17), Montreal, Canada, 8–11 August. Nurmikko-Fuller, T., Bangert, D., Dix, A., Weigl, D., and Page, K. (2018a). “Building Prototypes Aggregating Musicological Datasets on the Semantic Web”. Bibliothek Forschung und Praxis, 42(2), pp. 206–221. Nurmikko-Fuller, T., Bangert, D., Hao, Y., and Downie, J. S. (2018b). “Swinging Triples: Bridging Jazz Performance Datasets Using Linked Data”. Proceedings of the 1st International Workshop on Semantic Applications for Audio and Music, Monterey, USA, 9 October. Nurmikko-Fuller, T., and Pickering, P. (2021). “Reductio ad Absurdum?: From Analogue Hypertext to Digital Humanities”. Proceedings of the 32nd ACM Conference on Hypertext and Social Media (ACMHT21), Dublin, Ireland, 30 August–2 September. Orwell, G. (1949). Nineteen Eightyfour. Secker & Warburg. Pfleiderer, M., Frieler, K., Abesser, J., Zaddach, W. G., and Burkhart, B. (eds.). (2017). Inside the Jazzomat: New Perspectives for Jazz Research. Schott Music GmbH. Sacks, O. (1985). The Man Who Mistook His Wife for a Hat and Other Clinical Tales. Summit Books. Simpson, R. M. (2020). “Augustine as ‘Naturalist of the Mind’”. Proceedings of the 31st ACM Conference on Hypertext and Social Media (ACMHT20), Virtual Event, USA, 13–15 July. van Hooland, S., and Verborgh, R. (2014). Linked Data for Libraries, Archives and Museums: How to Clean, Link and Publish Your Metadata. Facet Publishing.

6

Future Directions

6.1

Preamble

In words attributed to Yogi Berra, an American baseball player: “It’s tough to make predictions, especially about the future”. There have been myriad predictions of the history of technology over the decades and centuries, some (at least seemingly) staggeringly accurate, the others absurd in their error: in 2004, Bill Gates (founder of Microsoft) predicted that the problem of spam emails would be solved by 2006.1 In 1995, Clifford Stoll told Newsweek: Visionaries see a future of telecommuting workers, interactive libraries and multimedia classrooms. They speak of electronic town meetings and virtual communities. Commerce and business will shift from offices and malls to networks and modems. And the freedom of digital networks will make government more democratic . . . Baloney.2 An example of people getting it right is an article (titled “When Woman Is Boss” from 1926, by the famous inventor Nikola Tesla.3 He seemingly correctly predicts smartphones (“You will communicate instantly by simple vest-pocket equipment”), drones (“Aircraft will travel the skies, study, reviewing the world that is unmanned, driven and guided by radio”), and environmental disasters. He even foresaw a global hypermedia system that could bridge all the world’s knowledge through interconnected information (Linked Data, or the Semantic Web, perhaps?): “When wireless is perfectly applied the whole earth will be converted into a huge brain, which in fact it is, all things being particles of a real and rhythmic whole”. The revolutions in communication technologies were also clear to Tesla: “We shall be able to communicate with one another instantly, irrespective of distance”. Not only that he had a vision for video conferencing platforms almost a hundred years before the pandemic introduced terms like “Zoom-fatigue” into the common vernacular: Through television and telephony we shall see and hear one another as perfectly as though we were face to face, despite intervening distances of thousands of miles; and the instruments through which we shall be able to do his DOI: 10.4324/9781003197898-6

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will be amazingly simple compared with our present telephone. A man will be able to carry one in his vest-pocket. And he predicted the new role of women, too: the average woman will be as well educated as the average man, and then better educated, for the dormant faculties of her brain will be stimulated to an activity that will be all the more intense and powerful because of centuries of repose. Woman [sic] will ignore precedent and startle civilization with their progress. It is easy to cherry-pick those of Tesla’s predictions that have come to fruition (and those by Stoll which have not). Cognitive bias ensures we notice examples we can equate to in our lived experience. And, as evidenced by Tesla’s inventions, the man was incredibly smart and insightful, so we surely cannot be surprised at the accuracy of his thinking? At this point, it may be worth explicitly noting that although the term “Digital Humanities” did not exist in 1926, Tesla was clearly an interdisciplinary thinker, bridging STEM and HASS: “He is an engineer, an inventor and, above these as well as basic to them, a philosopher”. So where will the future take us? It is hard to say beyond where I hope we will go. What we can say is that AI and machine learning are currently of huge interest to researchers, and there are no signs of that interest waning anytime soon (she says, immediately wondering whether Blade Runner was that far off after all). Researchers across the globe are increasingly beginning to understand that the technologies and the datasets under-pinning machine-driven analyses are often inherently flawed. Put simply, even highly sophisticated algorithms developed and used with data tainted with historical bias simply reproduce them. To this point, technological solutions have often sought to increase simplicity by eliminating ambiguity. Future research should seek ways to address fundamental societal problems by specifically developing methods for capturing ambiguity and vagueness. But it will do so by using various types of data. By capturing information from diverse cultures and contexts across time, space, and social, linguistic, cultural and ethnic boundaries, rich and complex data from the Humanities provides the best proving ground for experimental methodologies and technical solutions that have the potential to embrace uncertainty, fuzziness and ambiguity. It is not going too far to suggest that until we come to grips with data ambiguity our collective faith in data analytics is effectively misplaced. As data analysis is steadily shifting from human users to algorithms and software agents, the need to address this issue is becoming increasingly urgent. 6.2 The Non-linear Approach to Discussing Linked Data in the Digital Humanities Linked Data is a data publication paradigm. It is a method for representing and publishing data using existing Web architecture and technologies. Linked Data is

Future Directions 105 one possible way of providing information online in adherence to the FAIR data principles,4 but it is not the only way. Data aggregation is arguably the raison d’être for this method, but Linked Data is not the only technological solution that enables it. In this paradigm, information is represented as a graph, consisting of interconnected nodes and arcs, of data instances (such as people or places) and the relationships between them (such as connecting a person and a place through the relationships of the latter being the place of birth for the former): information is captured as interconnected triples. But, not all graph databases are triplestores. In summary, there are many different ways and methods for accomplishing the publication of information online in a machine-navigable graph. It is perfectly possible to implement a Linked Data project that is wholly offline, but benefits from the computational heavy-lifting of utilising a knowledge graph. This book has focused on discussing one possible method, Linked Data, which is a specific method, reliant on standards issued by the W3C – the main standards organisation for the Web, globally.5 More specifically, the discussion has focused on the use of the Linked Data method for publishing information in the context of Digital Humanities, an interdisciplinary field that sits at the intersection of Computer Science and the Humanities. Research in this area (as defined in Chapter 1) consists of myriad different niche investigations, but these tend to fall into one of two very broad categories. They are either the use of digital tools, software, and algorithms in conjunction with Humanities data, with the aim of tackling research questions that are usual or typical in the Humanities, albeit answered with the aid of computerised tools on a greater scale or at greater speed. Or, they are the application of robust Humanities approaches such as critical evaluation, ethical debate, or theoretically robust frameworks (e.g. the application of a Feminist lens, or through, say, a Marxist perspective) on the assessment and analysis of digital resources, tools, and methods. Linked Data projects in the Digital Humanities are often excellent examples of truly interdisciplinary work, completed by researchers of different domain expertise: these projects take on the task of representing information by capturing the relationships between data entities. This requires expertise in the form of both the technical skills of computer scientists (in the creation of RDF triples, for example) and the domain-expertise of Humanities researchers (in the identification and articulation of the data and its internal structures, and the inclusion of tacit knowledge): both are essential. There are undoubtedly some who possess both the domain knowledge and the technical skill to implement a fully fledged Linked Data project in the Digital Humanities, but for the vast majority, the old adage rings true: no man is an island. There is no technological reason why anyone choosing to establish a Linked Data project in isolation could not do so, but as we argued in Burrows and Nurmikko-Fuller (2020), to maximise the benefit of the technology, connecting to the information published by other projects is paramount. The utility of the Linked Data method for supporting and diversifying research in the Humanities is clear because these investigations are often the result of drawing connections between things (locating a person at an event, establishing details of interpersonal interactions, or illustrating how something has changed over time

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in a diachronic investigation, etc.), and then inferring further conclusions about those things. Humanities researchers have arguably invested network diagrams and analogue hypertext systems to support their investigations many times over: mindmaps are an example of such a methodology, and a widely used, generic one at that – the system developed by Pickering (and published in Nurmikko-Fuller and Pickering, 2021) is a concrete example of a specific implementation. Simpson (2020, July) unambiguously declared the cognitive processes of Humanities investigations to be interconnected and hypertext-like. Beyond that, the inherent flexibility and robustness of the RDF triple, and the absence of any technological limitations to the types of data entities and relationships that can be declared (that is to say, as long as the resulting knowledge graph does not contain any logical inconsistencies, the creator of the ontology and the implementer of the triples can have ultimate freedom in how they categorise their data and knowledge about the subject), Linked Data provides an ideal technical solution for representing the ambiguous, messy, and incomplete data of the Humanities. The decentralised graph means that information does not need to be forced into strict hierarchies: for example, there is no need to declare one book the epitome of a work, and have all other versions be secondary interpretations: rather, the graph enables us to represent all versions as equally significant but different manifestations of an intellectual piece. That having been said, the practical and pragmatic implementation for Linked Data projects is non-trivial, and in the absence of readily available and well-known tools (including tools with GUIs and WYSIWYG6 designs) can remain beyond the scope and possibility of Humanities scholars. The solution is an interdisciplinary research cluster, but there can be social, institutional, economic, and academic considerations that make the establishment of such research groups prohibitively difficult or even seemingly impossible. Lack of familiarity with Linked Data amongst the supporting and professional staff at higher education and cultural heritage institutions (e.g. those working in a central IT provision department) can further dissuade humanities academics or GLAM sector professionals from taking on the task of implementing Linked Data projects at any scale. Institutional policies and economic models can affect Linked Data projects even when they do exist. An example of this is the ElePHãT project, described and discussed in Chapter 3. This project focused on the investigation and development of a user-interface that would enable the querying (using SPARQL) of the collections, at the metadata level, of two very different digital libraries: the boutique and bespoke Early English Books Online – Text Creation Partnership project collection (EEBO-TCP), and the behemoth HTDL. The feasibility of this project was based on two considerations: first, there were sufficient schematic similarities between the databases of the two digital libraries (including categories such as person/author, title, publication place, ID number, etc.) to enable schema-level bridging; and second, tacit knowledge of specific individual parallels (such as the same named individual, the same publication or work, the same geographical location) contained within the respective databases to enable instance-level anchoring. At face value, the two digital libraries have many differences: one is huge, the other is tiny. For the HTDL, the metadata is largely generated through automated processes, the EEBO-TCP is manually curated by expert librarians.

Future Directions 107 From a Linked Data perspective, the main difference is one of institutional policy with regard to the Openness and accessibility of the data: at the metadata level, all 25,000 records of Phase I of the EEBO-TCP are publicly available. For the HTDL, only about a third of the metadata records are freely available, with a whopping 66% or so still restricted under copyright. The technical implementation of the ElePHãT project saw the combination of Linked Data (HTDL metadata) with Linked Open Data (EEBO-TCP metadata). In practical terms, when querying the project UI, the result displays those records that meet the query criteria and are available. It is possible to return results that are influenced and affected by unobservable records – in other words, it is potentially possible to show all the records that match the query, but not to show records which form part of the knowledge graph that connect two or more records together. To put this another way, let’s consider a hypothetical scenario as a way of a thought experiment. Let us assume there are two authors. They have both written a single-authored monograph, and both the volume and the metadata record are publicly available. Let us assume further that building upon the successes of their respective publications, the two have decided to co-author on a third volume. This one, however, is not publicly available, nor is its metadata record. All we can say from the accessibility point of view is that the volume exists, but no other details (such as topic, genre, publication place, etc.). Now, let us assume even further that we can query the aggregated knowledge graph for the works of authors who have collaborated at some point in time. At this stage, we are not focused on the domain or topic – we just want to know how prolific the act of co-authorship is in the works recorded by these two digital libraries. Although the third (co-authored) volume is not accessible, it forms part of the graph, and is the data point that connects the two authors. The results of the query would provide the details of the two single-authored monographs, and note the existence of the collaborated publication, but not provide the details or access to its content. From a user perspective, it can be difficult to assess or establish whether this omission is due to a failure of the user-interface, or of the underlying project. Rarely do users consider the root-cause of such access limitations to be institutional policy. Although Linked Data has many of the hallmarks of bringing forth the type of democratised information highway that tech-utopianists can only dream of, the business models of many heritage and memory institutions, publishers, and repositories serve to prevent its large-scale adoption. Significant players in the field of publishing scientific and research data have a financial incentive to prevent Open Data and Open Access, but it is these that Linked Data is dependent on. In many ways, the work of a Linked Open Data methodologist can only begin when information has been made FAIR. At the same time, the oligopolies of the Data Economy (Alphabet, Meta, etc.) utilise Linked Data methodologies to turn information into profit. In many ways, this presents a perfect example of how Linked Data is a potential privacy catastrophe waiting to happen. Indeed, there are many examples of personal, cultural, and other information which should not be made available through Linked Data publication. This technology, which is founded on the premise of combining complementary information from different, disparate data sources to address gaps in knowledge, is by its very definition, a method for removing

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anonymisation. Combined with the neoliberal, capitalist aspirations of large corporation to monetise on information, and the privacy paradox of users providing data, and the collection of information about user behaviours both overtly and covertly by social media platforms and search engines, Linked Data could be the kiss of death to any notion of privacy. It is, potentially, the ultimate tool for nefarious data science, just as much as it is for the tech-utopianist views. Linked Data, like any technology, is neither good nor bad, nor is it neutral (Kranzberg, 1986: 545). The neutrality of data capture and knowledge reputation is an unfounded fantasy. It is based on the erroneous idea that machines can in some way offer a method of objectivity to information categorisation or its analysis. It’s a fallacy, and one that has been shown, particularly in the context of the application of machine learning, to have potential for detrimental societal impact. The issues here are twofold: first, no matter how well-intentioned or valiant the effort, no information knowledge structure (database, spreadsheet, mind map or other) is independent of the underlying biases of the person or people who create them. That is not to say that misogyny, racism, or bigotry are rampant in all databases, but many have exhibited designs that have proven to be problematic: the gender binary is an example of one that seems at times to be ubiquitous. Similarly, data itself can only ever be retrospective. It is a snapshot of what has come to pass since its creation. For this reason, data collects bias – it is a historical bias that shows what has existed before, not the ideal or the potential future. This is not a bias of the algorithm, but of history. In the data used to train the algorithm, success had only been for people who had those demographic features. The potential for institutional or systematic bias is in both the tool and the data. These biases are often unobservable to users – as significant a reason as any for keeping the expert in the loop of statistical analysis, information representation, and data science. Even Linked Data is no substitute for human cognition. Observing overt bias such as bigotry can be relatively easy. It is a topic that enjoys some mainstream attention, there are training courses for those working with data to observe bias in data. Professionals working in the cultural heritage sector are undoubtedly aware of historical issues that affected the gender balance in the collections: there were times when women were not celebrated as artists, when gender-diverse people had no choice but to choose one or the other of a binary, and so on. In these cases, the data cannot possibly show the subtlety of lived experience, nor can we affect historical collecting and acquisition practices, and awareness of that bias is in many ways the best that we can do. But what of more subtle bias in the data? What of more subtle bias in our database designs? Chapter 4 discussed the notions of truth and bias, and the impossibility of having an objective truth expressed in an unbiased manner. Chapter 5 has examined the role of information structures in this process, and of the technical demands they place on the Linked Data workflow. It has illustrated one final benefit to the adoption of Linked Data: the possibility of maintaining both the specific and bespoke database structures, developed specifically for each and every minutiae of the dataset, whilst at the same time exposing the information, at least at metadata level, in accordance

Future Directions 109 with more universal standards and with the potential for interlinking. It is the possibility of harnessing the potential for interlinking and inference, of finding the unknown unknowns of the data universe, and enriching knowledge across data silos, without having to ultimately relinquish control over those specific data entities that are perceived as our own to have and to hold. In summary, Linked Data and Linked Open Data are methods for Humanities scholars, information disseminators, computer scientists, and everyone in between. The potential for aggregating information across the entirety of the connected and online human species seems akin to the realisation of the wildest of science fiction dreams. But it is not mere fantasy nor is it the remit7 of future generations. Linked Data is already here, it is already happening. What we need to do now is ensure that it is developed and implemented in a context of awareness and understanding of its potential for both utility and catastrophe. The critical evaluation of this methodology and all the projects and datasets that contribute to the vastness of the Linked Data Cloud has never been more important. 6.3 The Tech-Focused Summary of Linked Data in the Digital Humanities There are several interconnected technologies that play a role in creating, editing, managing, storing, and querying Linked Data. There is a cluster of acronyms, too. And, there are a few fundamental concepts, which seem simple, but can be philosophically complex. It’s a certain irony of teaching Linked Data that it is difficult to discuss these technologies out of the context created by the others: the composite parts of a technological implementation that enables us to create interconnected information are themselves interconnected. Where Linked Data differs from online information aggregation through interconnected pages, rather than point to websites or their individuals pages, on the Web of Data (the manifestation, or desired end result for publishing all data as Linked Data), URIs point to data entities (people, places, etc.), and the relationships between these entities. This is an absolutely fundamental shift – these unambiguous, absolute URIs point to abstract notions, intangible concepts, and the (in some cases temporarily bound and even ethereal) relationships between them. What this means in terms of the technological implementation is that sometimes there is no digital resource (e.g. website, image, audio file, or video) that the URI points to, but the URI represents an abstract concept. Knowledge graphs can also contain blank nodes, but as this seeps into the space of technological implementations, and will not be discussed further here. At the very basis of the Linked Data publication paradigm are an aim and a promise. The aim is to capture the information within and regarding a particular data domain using W3C standards so that the information itself can be published online in a format that makes it findable, accessible, interoperable, and reusable (ticking all the boxes of the FAIR data principles) but not just by human users but by software agents as well. The promise is twofold: first, that by doing so, this data

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can be connected up to other databases anywhere online that have complementary information in them (including those far beyond the reach of our current knowledge of disciplinary silos), and enriching our knowledge by enabling the discovery of so-called unknown unknowns – an infamous phrase uttered by the then U.S. Secretary of Defence Donald Rumsfeld at a U.S. Department of Defence news briefing in 2002,8 which enjoys an axiom-like notoriety amongst Linked Data practitioners. The second promise is of machine inference: the discovery of implicit knowledge from explicitly declared facts (a phrase used previously in Nurmikko-Fuller, 2018: 340). The aims, it turns out, are relatively easy to achieve, and there are many different workflows for the production of Linked Data that can be published according to the Five Star Standard. The promises are much less easy to point to, but there are examples from the Digital Humanities that have accomplished this, such as the Nomisma.org project and the myriad datasets it aggregates.9 Linked Data is based on a fundamental concept of interlinking data entities. At its core sits RDF – not a method or a tool, per se, but an abstract data model. A way of defining things, and the meanings those things have by way of representing the relationships those things have with each other. The most basic model of RDF consists of three parts, we call it the triple. Each consists of a Subject, predicate, and Object. Since the Object of one triple can be the Subject of another, it is possible to form interconnected chains of triples, resulting in a knowledge graph. RDF can be expressed in a number of different serialisations, or syntax. Of these, perhaps RDF/XML, Turtle (or the Terse RDF Triple Language) and JavaScript Object Notation for Linked Data (JSON-LD) are the most commonly used, at least at the time of writing, although in the future, this might be different. The key here is that data captured in one serialisation can be expressed in any other, without any loss of information. For this reason, practitioners in the field of Linked Data have strong, almost entirely personal reasons for preferring one over the other. As with many things relating to Linked Data, there are many standards that govern best practice, but there are also many different paths to the same goal, and the choice of which syntax to use is as personal as the choice of software. Those, for example, who have prior experience of XML (the Extensible Mark-Up Language) are likely to find that their existing skills are a good match for working with RDF/ XML. Similarly, those who have worked with JSON (JavaScript Object Notation) will find it easier to learn JSON-LD. My personal preference is for the Turtle syntax, because it is considered easier to read by human eyes and brain, and because it shares such strong similarities with the SPARQL query language. Therefore, all the examples in this book are expressed in Turtle. If the lack of creativity in the naming conventions of the Linked Data paradigm has yet to become apparent, nowhere is it as clear as with triplestores. This term applies to a graph database, and it is used to store triples. As stated by a very popular advertising slogan from the UK in the mid-to-late 1990s and the early 2000s: “it does exactly what it says on the tin”. Graph databases and triplestores can be referred to interchangeably in the vernacular of discussions between Linked Data practitioners, but strictly speaking, graph databases are more generalised,

Future Directions 111 and although they use graph structures (of nodes and arcs, etc.), triplestores are purpose-built for the storage, management, and retrieval of RDF triples using semantic queries. The cognitive leap Linked Data practitioners need to make in comparing relational databases to triplestores is that the latter contain nothing but RDF triples expressed in URIs. Specifically, HTTP URIs – the ones we know and love as part of existing Web architecture. This is because RDF is self-referencing. Let’s summarise this with an example. Let’s say we have a dataset about places. There is Helsinki, and Finland, and Europe. We can assign a URI to each of them – for the sake of simplicity and clarity of expression, they could be referred to as http:// example.org/Helsinki, http://example.org/Finland, and http://example.org/Europe. These are not genuine links and will not result in a website being displayed in the browser: they are identifiers, whose purpose is to denote the concept of those places. Again, for the sake of clarity, semantics could be embedded into these URIs (an approach that is not universally approved of in the Linked Data community). Embedded semantics (which can help make the process of manual debugging easier) can complicate the comprehension of the next point. So, instead, we will call them http://example.org/p9834tp348, http://example.org/le48utnop38, and http://example.org/ aei4gy4wyv. At this stage, the URIs carry no meaning to either human or machine. What is needed are additional URIs which point to characteristics and properties of the initial URIs. To make this point clearer, let’s use a cluster of URIs with embedded semantics. Note that the rdf:type property shown in the examples is a specific one that declares that the left-most URI is a data instance of the Class represented by the right-most URI. http://example.org/p9834tp348 rdf:type http://example. org/City. http://example.org/le48utnop38 rdf:type http://example. org/Country. http://example.org/aei4gy4wyvt rdf:type http://example. org/Continent. We can now see the first URI refers to a thing that has been defined, by Example. org to be something they define as a “city”, the second one to a country, and the third to a continent. We don’t yet know exactly how those concepts are defined, nor which city, country, or continent the URIs point to, nor how (if at all) they relate to one another. This additional information is incorporated into the system through the addition of more triples, but in this case, not all of the parts of all of the triples are URIs: to improve clarity, human-readable labels are added to show which specific instances these URIs point to, and to make the information easier for human brains to follow. These labels are not URIs, but clusters of characters referred to as literals: these are not machine-readable and so their main purpose is just to help the human user.

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The following formatting illustrates the clustering of information about a specific URIs together: http://example.org/p9834tp348 rdf:type http://example. org/City; http://example.org/p9834tp348 rdfs:label “Helsinki”. http://example.org/le48utnop38 rdf:type http://example. org/Country; http://example.org/le48utnop38 rdfs:label “Finland”. http://example.org/aei4gy4wyvt rdf:type http://example. org/Continent; http://example.org/aei4gy4wyvt rdfs:label “Europe”. To save us from repetition and to simplify the expression, we can remove the repeated URIs in the Turtle syntax without loss of information, resulting in a cleaner example: http://example.org/p9834tp348 rdf:type http://example. org/City; http://example.org/p9834tp348 rdfs:label “Helsinki”. http://example.org/le48utnop38 rdf:type http://example. org/Country; http://example.org/le48utnop38 rdfs:label “Finland”. http://example.org/aei4gy4wyvt rdf:type http://example. org/Continent; http://example.org/aei4gy4wyvt rdfs:label “Europe”. The connections between the entities are, again, captured by the addition of further triples. We could add a specific triple to each cluster in order to illustrate its relationship with the other. In the case of Helsinki, it would, for example, have a relationship with the country of Finland; and indeed, many different relationships, all of which we could represent as equally valid characteristics: We could specify that Helsinki is located within Finland, and that Finland, in turn, is situated within Europe, and use the same property for each triple. We could specify that this relationship is bidirectional, and that not only is Finland situated within Europe, but Europe has Finland within it. Or, we could specify that Helsinki’s relationship with Finland is unique as the capital city; or, we could assert them both. Or, we could choose to have two separate unidirectional properties, or implement one of a number of different alternative strategies. There are many different approaches and solutions, and at the same time, there are no technological restrictions to what kind of relationships can be asserted, as long as the graph must maintain an internal logic. The driving force behind these decisions is usually twofold: the desire to capture all the possible relationships in the domain, and second, to facilitate the navigation of the graph most efficiently using SPARQL queries.10

Future Directions 113 SPARQL enables us to ask questions that navigate the knowledge graph. We can do so from any point in the graph, to any other. That is to say, if there are connections in the RDF triples, then we can start the question from any Class, and traverse the graph to any end point. There is no need to start at the top of a hierarchical tree structure, and navigate down to an increasingly specific point. The graph is decentralised, the graph is connected. Furthermore, the underlying graph can include data from beyond the system itself: we can ask a Linked Data project questions where part of the answer is determined by information available from an external data source. Examples of Linked Data project questions that have required more than one singular dataset to answer have been the likes of: • Which performances of Body and Soul in a specific key [dataset 1] were recorded in a specific place [dataset 2] by artists that played with a particular artist [dataset 3]? For example, identify recordings of Body and Soul in D-flat made in New York City by artists who played with Roy Eldridge during their career (Nurmikko-Fuller et al., 2018); • Find all the [written] works contained in [dataset 1] for authors who have at least once published on the subject of “Political science” [dataset 2] (Page and Willcox, 2015); • Use lyrics sung by Siegfried (a character in Richard Wagner’s Der Ring des Nibelungen) on Genius.com [dataset 1] as the basis for identifying complementary information (text companions [dataset 2], audio [dataset 3], notations [dataset 4], images [dataset 5]) across various different data sources (Nurmikko-Fuller and Page, 2016); and • Find all fictional monsters that share physical characteristics of claws, tongue, or skin with an animal, and that also live in a river habitat (Wang et al., 2019). The trick here is to recognise two factors: first, that these questions are not unreasonable, indeed, they are legitimate research questions that a Humanities scholar would quite rightly wish to pursue, and second, that whilst SPARQL here is not enabling a task, which would not be possible for a human brain to answer if given the necessary information, it is enabling a data-gathering process at a much greater scale and speed than would have been possible for a human scholar alone. Now, back to our URIs! So far we’ve seen that http://example.org/aei4gy4wyvt (Europe) is an instance of the information category (Class) of http://example.org/ Continent, but how do we know what concept http://example.org/ Continent refers to? This is where ontologies come in. The structure of the database, all information categories, and all instance-level data, are expressed in the same way. But how do we know what the structure is? How do we capture the meanings in Linked Data projects, which are, after all, about nothing if not semantics? This is where ontologies, vocabularies, and schemas enter centre stage. These three words again refer to very similar concepts, and are often used synonymously or interchangeably. The main difference here is that arguably, vocabularies represent only the types of entities that exist in the domain,

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whilst ontologies and schema also – crucially! –capture the relationships between those entities. For the sake of clarity and simplicity, the discussion here refers to “ontologies” exclusively. But which triplestore is the best? How does one pick the right one? These questions, at face value, seem reasonable, and they are ones that pop up often. The desire to find the most suitable tool is a natural urge, and in the best scenario helps reduce wasted time and can be presented as the most cost-effective solution. But “best” is inherently subjective, and the solution that might be ideal for one project or one programmer is unlikely to be the same for another. Case study examples in this book have used different triplestores (e.g. Virtuoso11 and Blazegraph),12 and these have been selected for reasons such as cost (both are free to use), perceived ease of use, existing expertise in the investigatory team, and even the accident of prior selection (joining a collaborative project where the other team had already installed and successfully used Virtuoso). Recommendations are a valuable and important part of the academic discourse, but too often we are forgetful of explicitly, critically reflecting on the reasons why we use the tools we use. Furthermore, once a particular tool has been learnt or a specific methodology perfected, there is a benefit in reuse, as the time invested in acquiring the skill or knowledge does not have to be spent again. What this does mean is that we are biased (even if with good reason) to reuse an approach or tool we once (perhaps for largely arbitrary reasons) happened to learn. This is also true of other aspects of the Linked Data workflow, such as the (re)use of ontologies, and other design decisions. As described in Chapter 4, ontologies are formalised structures, which explicitly state all the possible types of things and relationships between things that can exist in the mapped domain. They are machine-readable documents, written in RDF. They act as the schema for the graph database: showing information categories and the connections between. As such, they can be designed in many different ways, but in the Digital Humanities, two types are more prevalent than others: the datadriven approach, such as the ones we used for the JazzCats case study in Chapter 5, or universal, top-down models such as the CIDOC CRM described in Chapter 4. Because ontologies reflect the understanding and knowledge the people designing them have of the mapped domain, they inevitably include some degree of cognitive bias, making documentation a particularly important, if arduous task. Experts in any FOR and scholarship inherently and often unconsciously believe in the uniqueness of their datasets and research questions. To the outside observer, this can be difficult to reconcile. How, or why, for example, would the metadata held in a library for a book differ from that of a volume that forms part of a museum collection? There is only so much that can be said about a book, surely (author, publisher, date of publication, topic, subject, genre, classification, shelf mark, collection, owning institution, page number, material, size, the list is actually vast and I am being deliberately facetious)? The truth is there is a great similarity in the ways in which information about Things (any things!) is collected and recorded even across vastly different disciplines. Human beings educated in the Western tradition of knowledge capture and representation are taught to categorise and catalogue the world and that in turn affects the way in which we observe the

Future Directions 115 world. To have an appreciation of epistemology is then to have, in some way, an appreciation of information provenance. But not often do we stop to question why it is that we know that we know, or whence the data or information we are processing comes from, and what inherent biases, omissions, perspectives, and so on might be embedded within them. And this is true of areas of study far further removed from each other than libraries and museums, for what field that would not value (even demand!) evidence of the data provenance chain? Who wouldn’t want to know how you know what you know? Another consideration is at the level of methodology. How do you know what to do? In some cases, we have done it before, and subsequently seek to repeat a successful workflow. We also consult each other, through conversation, question and answer forums, through systematic reviews of published academic papers. We seek to determine what has been successful before, and apply it to a new context. Or we develop new ways to make an existing process manifest quicker or at a greater scale. But we also default to points of prior expertise even engaging in new endeavours. A ubiquitous example of this is in the process of something as innocuous as deciding on a type of database: the decisions are driven often by subjective aspects such as the programming languages used to manage them, cost, and existing familiarity. This is also true when selecting graph databases, and triplestores. The best recommendation is thus to identify the tools and workflows that best suit the research team’s existing skills, goals, aims, deliverables, available resources, funding, institutional policy, and desired outcomes. There are many ways to achieve Five Star Linked Data, and much in terms of freedom to pick the most suitable approaches and workflows. The technical challenges in the future development of Linked Data will undoubtedly include the development of new tools and new user-interfaces (such as the prototype GUI reported on by Gatti et al., 2022) that enable interaction and creation of data as RDF for novices and even self-identifying luddites (Chapter 1). Furthermore, the ever-increasing size of the Linked Data Cloud has shown a pattern of exponential growth in the number of distinct projects that contribute to it. Future developments in the technical aspects of Linked Data are likely to be steps towards a greater combination of algorithms that fall into the categories of AI and machine learning; reasoners of increasing sophistication will navigate increasingly complex graphs until the techno-utopian dream of the bridging of all the information online will be realised. 6.4

Conclusion

Linked Data has potential. Linked Data works. But it is not just typing a word into a search engine. Linked Data is more than a set of recommendations for best practice; it is an information publication paradigm with a series of W3C standards for implementation and quality. It is a method for connecting complementary datasets, and for representing interconnected facts in graph structures. The technology has already been utilised by many of the most powerful entities involved in online publication, but relatively little has trickled down to the user. Today, power computational processes

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affect the way we store, discover, and retrieve information online, but much of the heavy lifting is happening behind the scenes. Other technologies and approaches are enjoying their moment in the limelight of public interest and overt engagement: at the time of writing, these are predominantly AI and machine learning. Chapter 1 situated the discussion of the use of Linked Data in the context of interdisciplinary research in general, and of the Digital Humanities in particular, which sits at the intersection of HASS and STEM. Chapter 2 flagged one of the major considerations of using Linked Data, and in doing so at a time when we live in a Data Economy: Linked Data has the potential to be a privacy catastrophe. It is not uniquely so: other technologies could be used to produce the same outcomes, profiling, and analysis as Linked Data. Surely then the solution is not to avoid using the methodology, but to engage in the process of information aggregation and data science in thoughtful and ethical ways? Or should we take deliberate steps to ensure that new generations of up-and-coming data scientists and programmers, and their managers and supervisors, are taught to engage with data in a responsible and ethical way? And if so, how? Will true change only be possible once those in a position to implement legislation, regulation, and develop policies embrace these aims and values as a priority? Chapter 3 was an opportunity to discuss the plethora of manifestations and forms data takes. Although we often refer to the term as if it is a monolith, “data” is nuanced and consists not of one big thing but of what seem to be endless instances of smaller things, each with some degree of idiosyncrasy and uniqueness. Research and academic cultures can also muddy the waters. Chapter 4 illustrated how biases can creep into knowledge representation, whilst Chapter 5 has considered the demands created by existing information storage solutions from spreadsheets to relational databases to triplestores. The case studies in these chapters serve to show that the implementation of a complete Linked Data workflow is possible using Open Source and off-the-shelf software solutions. It is within the grasp of anyone regardless of whether or not they have programming skills or proclaim themselves a luddite. The future direction of Linked Data in the Digital Humanities is not easy to predict, but in the spirit of joyful pessimism, two things are likely: first, as more and more academics, researchers, and interested members of the public become familiar with the method and their eyes are open to its potential we will see an increasing number of them adopting the Linked Data paradigm. They will quickly realise how this technology can serve them. At the same time, this increasing enthusiasm and uptake will inevitably highlight that there are different aspects to Linked Data, all of which are difficult, but, inevitably they come to understand that successfully confronting the challenges inherent in any of the aspects of the Linked Data paradigm increases the pay off. Linked Data is hard but it’s worth the effort. Some might thrive on ontological development, while others would rather focus on projects that automate workflows for large-scale (and speed!) data aggregation. Second, the converts will better understand how the current enthusiasm with AI and machine learning will lead to the development of “smarter” and more efficient

Future Directions 117 software that will be able to query and infer knowledge over the interconnected graphs of the Linked Data Cloud with increasing sophistication. In other words, it has the potential to bring us one step closer to the vision of the Semantic Web. At that point, it will expose the true extent to which historical bias and Western knowledge structures dominate the fields of information representation and data science. We will have two options then: either start a valiant fight for equity in information representation and just sovereignty over it or accept the perpetuation of the rampant intellectual colonialism of the West. But what if we saw greater and more diverse representation in the world of Linked Data practitioners and ontological developers? As with all of academia, and across the powerhouses of policy development, the inclusion of people of different races, genders, cultures, languages, socio-economic backgrounds, and disciplinary domains could (radically) change the landscape. Would this be a way for us to leverage the richness of all different ways of perceiving the world, rescuing those that are about to disappear, and reviving, where possible, those that have been lost? In his 2008 TED Talk,13 anthropologist Wade Davis said: All human populations share the same raw human genius, the same intellectual acuity. And so whether that genius is placed into technological wizardry has been the great achievement of the West or by contrast, into unravelling the complex threads of memory inherent in a myth, is simply a matter of choice and cultural orientation. From a technological perspective, large-scale adoption of Linked Open Data could enable us to facilitate a revolutionary paradigm shift in information publication: to represent all these different views of truth as equally valid. But are existing socio-economic, or power-driven paradigms simply too entrenched to prevent us from ever doing so? With thoughtful, critical engagement mitigating risks, the pros could outweigh the cons. Surely it’s worth giving it a go? Notes 1 www.nytimes.com/2004/01/26/business/gates-predicts-that-spam-will-go-away.html. Accessed 27/05/2022. 2 www.newsweek.com/clifford-stoll-why-web-wont-be-nirvana-185306. Accessed 27/05/2022. 3 https://babel.hathitrust.org/cgi/pt?id=mdp.39015056079984&view=1up&seq=219&q1= Tesla. Accessed 27/05/2022. 4 Information published as W3C-compliant Linked Data is Findable, Accessible, Interoperable, and Reusable. 5 www.w3.org/. Accessed 09/05/2022. 6 WYSIWYG is an acronym that refers to a type of user interface. It means “What You See Is What You Get”. Most of us will be familiar with it in tools like MS Word or Google Docs: user-interfaces where you can affect what you see on screen by clicking an icon. We could compare that to something like LaTeX text editors, where the changes to the document (making the text bold or in italics, for example) is achieved through a process of marking up one document (.tex) and then rendering those changes into a another (.pdf).

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7 In the British sense, of being a task or area of activity assigned to a specific individual or organisation. 8 www.youtube.com/watch?v=REWeBzGuzCc&ab_channel=CNN. Accessed 20/05/2022. 9 https://nomisma.org/datasets. Accessed 20/05/2022. 10 SPARQL (the SPARQL Protocol and RDF Query Language). What a name! This recursive acronym (which contains “SPARQL” in it) refers to what could be considered the equivalent for RDF triples of what SQL (Structured Query Language) is for relational databases. Indeed many of the commands between SPARQL and SQL are the same (such as SELECT, ASK, CONSTRUCT, etc.), and SPARQL can be used to edit, manage, delete, add, and change RDF triples held in a graph database (rather than a relational database) or a triplestore. 11 https://virtuoso.openlinksw.com/. Accessed 27/05/2022. 12 https://blazegraph.com/. Accessed 27/05/2022. 13 www.ted.com/talks/wade_davis_the_worldwide_web_of_belief_and_ritual/transcript. Accessed 29/05/2022.

Bibliography Burrows, S., and Nurmikko-Fuller, T. (2020). “Charting Cultural History through Historical Bibliometric Research: Methods; Concepts; Challenges; Results”. In Schuster, K., and Dunn, S. (eds). Routledge International Handbook of Research Methods in Digital Humanities. Routledge. Gatti, T., Nurmikko-Fuller, T., Pickering, P., and Swift, B. (2022). “Having a Ball: A Linked Data Approach to Fancy Dress in Colonial Australia”. Proceedings of the International Digital Humanities Conference 2022 (DH2022), Tokyo, Japan, 27–29 July. Kranzberg, M. (1986). “Technology and History: ‘Kranzberg’s Laws’”. Technology and Culture, 27(3), pp. 544–560. Nurmikko-Fuller, T. (2018). “Publishing Sumerian Literature on the Semantic Web”. In Juloux, V., Gansell, A., and Di Ludovico, A. (eds). CyberResearch on the Ancient Near East and Neighboring Regions. Brill. Nurmikko-Fuller, T., Bangert, D., Hao, Y., and Downie, J. S. (2018). “Swinging Triples: Bridging Jazz Performance Datasets Using Linked Data”. Proceedings of the 1st International Workshop on Semantic Applications for Audio and Music, Monterey, USA, 9 October. Nurmikko-Fuller, T., and Page, K. R. (2016). “The Linked Semantic Network That Is Transforming Musicology”. Proceedings of the 1st Workshop on Humanities in the Semantic Web – WHiSe, Co-located With the 13th Extended Semantic Web Conference 2016 (ESWC) – Heraklion, Crete, Greece, 29 May. Nurmikko-Fuller, T., and Pickering, P. (2021). “Reductio ad Absurdum?: From Analogue Hypertext to Digital Humanities”. Proceedings of the 32nd ACM Conference on Hypertext and Social Media (ACMHT21), Dublin, Ireland, 30 August–02 September. Page, K., and Willcox, P. (2015). ElEPHãT: Early English Print in the HathiTrust, a Linked Semantic Worksets Prototype. Final Report for Workset Creation for Scholarly Analysis: Prototyping Project, University of Illinois at Urbana-Champaign. Simpson, R. M. (2020). “Augustine as ‘Naturalist of the Mind’”. In Proceedings of the 31st ACM Conference on Hypertext and Social Media (ACMHT20), Virtual Event, USA, 13–15 July. Wang, Q., Nurmikko-Fuller, T., and Swift, B. (2019). “Analysis and Visualisation of Narrative in Shanhaijing Using Linked Data”. In Proceedings of the International Digital Humanities Conference 2019 (DH19), Utrecht, Netherlands, 9–12 July.

Glossary

Algorithm A finite list of very rigorously defined instructions so that a step-bystep procedure for solving a problem, accomplishing a task, or performing a computation can be achieved. Australian Research Council (ARC) The primary non-medical research funding agency of the Australian government. Big Data A term used to refer to large datasets, which cannot be readily interpreted or analysed by humans, but require computational tools. CARE A data principle developed as the Indigenous response to the FAIR. CARE promotes the inclusion of people and human-centric considerations into the publication of data, and refers to Collective Benefit, Authority to Control, Responsibility, and Ethics. CARE promotes Indigenous data sovereignty, and whilst it has many purpose-driven aspects (such as the need for evidencebased policy development), the bulk of the focus is on people-oriented principles, such as relationships, accountability, and the ability of stakeholder groups to exercise control over data that pertains to them. Comma Separated Value (CSV) A delimited text file that uses a comma to separate values. A spreadsheet and a CSV file can contain the same data in the former, it is displayed in cells (columns and rows), in the latter as a long line of text, where each value is not separated from the next one by a comma. Creative Commons (CC) A not-for-profit organisation and international network devoted to improving access to creative works by making them available through licences that enable legal sharing. Database Management System (DBMS) A software system that’s used to store data, as well as retrieve it and even run queries. Serves as the interface between the human end-user and the database. Domain In the Linked Data world, a Domain serves as the Subject for a property, that is to say, properties and predicates run from Domain (Subject) to Range (Object). In terms of Web architecture, a domain refers to a distinct subset of addresses sharing a common suffix. An academic domain is a recognised area of research or study. Extensible MarkUp Language (XML) As the name suggests, XML is a MarkUp language. It is used to store and transmit data that’s been encoded in a format that is accessible to both humans and software agents.

120

Glossary

FAIR A data principle that states that data should be Findable, Accessible, Interoperable, and Reusable. It also addresses metadata and supporting digital infrastructure, such as search engines. FRBRoo FRBR-Object-Oriented Graph Database A graph database stores information as nodes and relationships, rather than as a collection of tables or documents. These graph structures are navigated using semantic queries. HathiTrust Digital Library (HTDL) The HathiTrust Digital Library is a largescale digital repository. Its collections are an amalgamation of collections from various research libraries, including content digitised by Google Books. International Organisation for Standardisation (ISO) A worldwide federation of national standards bodies. Knowledge Graph Also referred to as semantic networks, knowledge graphs represent data entities (such as people, places, events, or concepts) and the relationship between those data entities, forming an interconnected (often decentralised) graph or network. Luddite Now a blanket term to describe people who dislike computers and digital technology, the origins of the phrase date back to the 19th century. It was named for a movement of labourers who protested against the introduction of mechanised processes and machines, particularly in the textile industry. Mesopotamia Culturally and linguistically diverse historical region, which geographically constitutes the area of modern-day Iraq and the surrounding areas. Metadata A term that refers to data about (other) data. It applies to all kinds of files and resources, and often (but not exclusively) constitutes details such as creation date, size, and creator. Methodology A study or critical evaluation of a research method; can also refer to a system of methods used and applied in the context of a particular academic investigation or research process. Motif A dominant or recurring idea, especially in an artist’s work. Non-proprietary Generic, or something not registered or protected as a trademark or brand name; data published as an MS Excel is in a proprietary format because the file can only be read using a specific piece of software. The equivalent data in a CSV file is in a non-proprietary format. Ontology In the Linked Data world, ontologies are files or conceptualisations that identify concepts and categories in a subject area or domain, show their properties and the relationships between these categories of subjects, and thus provide a self-referencing representation of the domain in question. In the Humanities, the term “ontology” is often used in the context of philosophical study of being: in this book, the word is only used to refer to the former (machine-readable document). RDF/XML RDF/XML is a syntax to express an RDF graph as an XML document. Semantic shift The phenomenon of language change regarding word usage, whereby meanings of words can change over time, sometimes becoming unrecognisable or socially unacceptable. SQLite3 C library that provides a lightweight disk-based database.

Glossary

121

Sumerian Linguistic isolate; a now-dead language used in Mesopotamia for several millennia BC. Syntax In the study of languages, syntax refers to the study of how sentences and phrases are formed from smaller linguistic units such as words. In Computer Science, the term refers to the structure of statements in a computer language. Tabular data Information organised in columns and rows. Tech Utopianism An ideology (or ideologies) based on the concept that advances in technology, engineering, and science could bring about a better future (even a Utopia), or at least contribute to the advancement of a utopian ideal in a particular context. Triplestore A purpose-built database for the storage of RDF triples. Data can be retrieved, navigated, edited, and so forth, using semantic queries. They are a specific type of graph database. Wisdom literature A genre of literature common in the ancient Mesopotamian writings, consisting essentially of words of wisdom, that is, sayings, axioms, proverbs, and guidance. Workflow A sequence of steps or processes that need to be completed in a specific order so that a piece of work can be completed. Xquery Language for querying XML data.

Index

6–9 meme 66 Adobe Creative Cloud 51 Adobe Systems 83n7 AHRC see Arts and Humanities Research Council (UK) AI see Artificial Intelligence Akkadian 83n24, 84n28 algorithms: AI and machine learning 2, 115; false objectivity of 67, 104; glossary 119; Humanities data and 105; increasing sophistication of 19; racism and bias of 24, 108; surveillance by 36; as term, origin of 10 algorithmic concepts 11 al-Kindi 10 Al-Khwarizmi, Mohammad ibn Musa 10 Allemang, D. xiii Alphabet company 23, 83n7, 107 Analytical Engine 10 anonymisation 32–33, 37, 108 anonymity on the internet 23 AR see Augmented Reality ARC see Australian Research Council Arlington National Cemetery 24 Artificial Intelligence: ethics of 56; human cognition as a process, attempts to recreate by 86; human faces created by 2; machine learning and 2, 115 Arts and Humanities Research Council (AHRC) (UK) 7, 9–10, 52 Augmented Reality (AR) 24 Australian Research Council (ARC) 7, 52–54; glossary 119 Babbage, Charles10 Banu Musa brothers 10 Barber, Elizabeth 34 Barry, Hank 42

Barth, S. 29 Bayt-al Hikmah (House of Wisdom) 10 beauty as being in eye of the beholder 66, 70, 83n8 Beecher, Henry Ward 67 “beer, free as in” 51 Beer, D. 93 Bentham, Jeremy 27 Berners-Lee, Tim xi, 12, 55 bias 66–83; algorithmic 24, 67; cognitive 104, 114; in database design 108; deep-routedness of 81; gender 73; historical 104, 108, 117; inherent 82, 115; internal 70; in ontological modelling 73; in ontologies 68, 69–71; ontologies and 15; overt or covert 68, 108; racist 24; in recommendation 99; removing 56; in Software of Expertise 92; in tool recommendations 90–93; unavoidable 75; unchecked 74; underlying 108; workflow 99 Bibliographic Metadata Ontology (MODS) 60 Bibliographic Metadata Ontology (extension to MODS) (MADS) 60, 72 Biblioteca Escolar Digital (CITA) 32 ‘Big Bad’ 23 Big Data 44, 93; glossary 119 bigotry 108 biometric information 31 Bishop, C. 2, 49 Body and Soul discography 95–96 Bonewell, Perry 88 Bongiorno, F. 57 Brand, Stewart 53 Brewster, C. 16, 67 British Broadcasting Corporation (BBC) 6, 27

Index Buddhist thought 87 Burrows, S. 105 Busa, Roberto 10 Bush, Vannevar 17, 89 Byron (Lord) 11 Cambridge Analytica 26–27, 34–35 CARE (Collective benefit, Authority to control, Responsibility, Ethics) data principles 36, 56; glossary 119 Cauberghe, V. 29 CC see Creative Commons CC Attribution-ShareAlike 4.0 International Licence 58 CEO see Chief Executive Officer Chalmers, D. 87 Charteris, Martin (Sir) 57 Chief Executive Officer (CEO) 24 Chomsky, [N.] 86 CIDOC CRM see International Council of Museums (ICOM)’s Conceptual Reference Model CITA see Biblioteca Escolar Digital Clark, A. 87 cloud computing 28 Cloud, the 32, 100; Adobe Creative Cloud 51 Collective benefit, Authority to control, Responsibility, Ethics see CARE comma separated value (CSV) 12, 53, 95–96; glossary 119 Commonwealth Scientific and Industrial Research Automatic Computer (CSIRAC) 11 copyright law 42–43, 51 copyright restrictions 52, 59–61, 96, 107 COVID-19 29 Creative Commons (CC) 51; CC Attribution-ShareAlike 4.0 International Licence 58; glossary 119 CSIRAC see Commonwealth Scientific and Industrial Research Automatic Computer CSV see comma separated value Cunningham, G. 48 D2RQ Platform 96 Dante’s Inferno 1 Database Management System (DBMS); glossary 119 Dallas, C. 12 Dark Ages 10

123

dark energy 48 dark matter 48 Dark Web 27 data 43–50; accessible and inaccessible, combining 58–59; ambiguous 47–48; incomplete 48–49; interpreting of, as Humanities research output 44; location 33–34, 61; messy 49–50; quantitative 43; quantitative analysis of 44; tabular 95–96; unreproducible 45–46; unstructured 46–47; see also Big Data; Linked Data; metadata; Open Data; Web of Data data aggregation 36, 62 data capture 95–96; Linked Data and 19, 110, 113–114; supposed neutrality of 108 Data Economy 19, 26–27, 31, 37–38, 53, 116 data fetishist 33 data producers and consumers 30–32 data profiling 25–26, 35, 116 data scraping see scraping DBMS see Database Management System DBpedia 32 De Jong, M. 29 de la Rosa, J. 2 Descartes, [R.] 86 DHer see digital humanities researcher Digging into Data Challenge 43 Digital History 8 Digital Humanities: computer science and 10–12; interdisciplinarity and 2–4, 6, 18–19; linked data and 12–18; linked data in 109–117; non-linear approach to discussing linked data in 104–109; public face of 10; research funding for 7–8; Rodin’s Gates of Hell and 1–2, 18; Rodin’s Thinker and 1; two cultures of 9, 19 digital humanities researcher (DHer) xv, 96, 100 diversity and ambiguity, reductionist attempts to eliminate 81 diversity and inclusion 36; gender 74 domain 1–2, 15, 113; Cross Domain 32, 39n27; data 109; disciplinary 117; glossary 119; mapped 114; range and 68; subdomains (HASS) 44, 48; subject 69 domain experts and expertise 6, 48, 105 domain ontologies 68

124

Index

domain-specific outcomes 4, 68, 71 Drupal 88 DuCharme, B. xiii, 67, 69, 97 Early English Books Online Text Creation Partnership (EEBO-TCP) 59–61, 106–107 Early English Print in HathiTrust, Linked Semantic Worksets Prototype (ElePHãT project) 59–62, 63, 107 EEBO-TCP see Early English Books Online Text Creation Partnership Electronic Text Corpus of Sumerian Literature (ETCSL) 76–80 ElePHãT project see Early English Print in HathiTrust, Linked Semantic Worksets Prototype Engineering and Physical Sciences Research Council (EPSRC) (UK) 7, 9–10 EPSRC see Engineering and Physical Sciences Research Council (UK) Estill, L. 36 ETCSL see Electronic Text Corpus of Sumerian Literature ethics 23–38; CARE and 56, 119; question of 36–37 EU see European Union European Economic Area (EEA) 28 European Union (EU) 28 Extended Mind thesis 87 Extensible MarkUp Language (XML) 59–60, 93–94, 110; glossary 119 Extensible Stylesheet Language Transformations (XSLT) 93 Facebook xi, 23, 25–27, 31, 92; Cambridge Analytica scandal 27, 34–35; #DeleteFacebook 34; domination of 44; Zuckerberg 27, 70 facial recognition 2 FAIR (Findable, Accessible, Interoperable, Reusable) principles 53, 55, 56; glossary 120 Fear of Missing Out (FOMO) 29, 35 Field of Research (FOC, for the ARC) 7 Filter Bubble 31 Findable, Accessible, Interoperable, Reusable see FAIR principles FindFace 24 Five Star Standard 12, 52–56, 95, 110, 115 FOAF see Friend of a Friend FOMO see Fear of Missing Out Foucault, Michel 16–17

Foucault Effect (Beer) 93 FRBR see Functional Requirements for Bibliographic Records “free, as in beer” 51 free, meaning of 53 Free Software Foundation 51 Friend of a Friend (FOAF) 74 Functional Requirements for Bibliographic Records-Object-Oriented (FRBRoo) 45, 60, 77, 79; glossary 120 Galleries, Libraries, Archives, and Museums (GLAM) sector 47, 56–57, 68, 87–89, 99, 106 Gatti, T. 115 gender balance 108 gender bias 73 gender binary 75, 79–80, 108 General Data Protection Regulation (GDPR)(EU) 28 Genesis, Book of 75 Getty Vocabularies 36 GLAM see Galleries, Libraries, Archives, and Museums (GLAM) sector Global Positioning System (GPS) 33 GPS see Global Positioning System Graphical User-Interface (GUI) 98, 106, 115 Greenfield, A. 24, 46 Group of Eight (Go8) 8, 20n12 Gruber, T. 15, 67 Hacker’s Conference 1984 53 Hafeez, Kaamran 23 Hall, Wendy (Dame) 11–12 HASS see Humanities, Arts, and Social Sciences Hastings, Reed 70 HathiTrust Digital Library (HTDL) 59–62, 65, 106–107; glossary 120 Hendler, J. xiii historiographies, of sciences and humanities 9 Hocking, Jenny 57 Hooland, S. van xiii, 91, 94 HTDL see HathiTrust Digital Library Humanities 11; methodology of 9; see also Digital Humanities Humanities, Arts, and Social Sciences (HASS) 2–10, 16, 19, 44, 48, 104, 116 HTTP see HyperText Transfer Protocol HTTPS see Hypertext Transfer Protocol Secure

Index hyperdata 16 hypernyms and hyponyms 68 hypertext 19, 44, 106; graph-based 89; obsolete 99; systems 11 HyperText Transfer Protocol (HTTP) URIs 13–14, 32, 94, 111–113 Hyppönen, Mikko 26, 33 Hyvönen, E. xiii, 67 IBM see International Business Machines Corporation Index Thomisticus 10 influencers 28 information retrievers 31 Instagram 28, 30, 35, 44 interdisciplinary 2–5 International Business Machines (IBM) Corporation 10 International Council of Museums (ICOM)’s Conceptual Reference Model (CIDOC CRM) 45–46, 69, 71, 77–79 JazzCats 91, 94–97, 99–100 JavaScript Object Notation (JSON) 110 JavaScript Object Notation (JSON) for Linking Data 110 J-DISC see Online Jazz Discography Jenner, Kylie 28 JISC see Joint Information Systems Committee Joint Information Systems Committee (JISC) 61 JSON see JavaScript Object Notation JSON-LD see JavaScript Object Notation (JSON) for Linking Data Kant, [I.] 86 Kardashian family 28 Kardashian, Kim 28 Kerr, John 57 Kranzberg’s First Law 67, 88, 100n4 Kawase, R. 30 knowledge 13; capture of 114; domainspecific 68; theory of 17 Knowledge Graph 17, 55, 93–95, 105–107, 109; glossary 120; interconnected, knowledge inferred from 117; SPARQL and 97–98, 113 Knowledge Representation (KR) 55, 67, 88; bias in 116; Networked Environment for Personalized, Ontology-Based Management of Unified Knowledge (NEPOMUK)

125

Contact Ontology 74; Networked Knowledge Organization Systems (NKOS) 36; systems of 18 knowledge reputation 108 knowledge structures 99; Western 28 KR see Knowledge Representation LGBTQIA+ (Lesbian, Gay, Bisexual, Transgender, Queer, Intersex, Asexual, Pansexual) 74 Liccardi, I. 34 Linked Art 69 Linked Art Model 69 Linked Data: ambiguity and vagueness using 18; case study, Sumeria literary compositions and 75–77; categorization of projects 20n10; Data Economy and 31; demands of data and 92–100; divided data and 44; era of 31; flexibility of 19, 94; future directions 103–117; Humanities and 12–19; information exchange facilitated by 72; interconnectedness of 103; interconnected thinking via 88–90; JazzCats as example of 91; Kranzberg’s First Law and 67; Linked Open Data and 54–56, 62; new knowledge inferred via 87; ontological modelling for 70; ontologies 69; paradigm 67; potential for privacy disaster posed by 32–33, 107, 116; practical considerations for implementation of 106; privacy and ethical issues raised by 26, 36–37, 107, 116; problems potentially solved by 48; publication paradigm 82, 93, 104–105; raison d’etre of 32–33; sceptics 98; SPARQL and 98; tech-focused summary of digital humanities and 109–115; workflow 91, 116; see also Five Star Linked Data Standard LinkedIn 44 Linked Jazz project 95, 97, 100 Linked Open Data 92; community 57; Linked Data and 54–56, 62; Five Star Standard for 52, 53; ontologies 69 Linked Open Data Cloud 32 Linked Semantic Worksets Prototype see Early English Print in HathiTrust, Linked Semantic Worksets Prototype

126

Index

Lovelace, Ada 10–11, 19 Luddite 5, 115, 116; glossary 120 MADS see Bibliographic Metadata Ontology (extension to MODS) March, E. 29 Marx, Karl 27 Marxism 73, 105 memes 201n1, 23, 69, 82n1 Memex 17, 89 memory institutions 87 Menabrea, Luigi 11 Mesopotamia 75, 79, 81, 83; glossary 120 Meta (formerly Facebook) 107 metadata: bibliographic 79; Bibliographic Metadata Ontology (MODS) 60; Bibliographic Metadata Ontology (extension to MODS) (MADS) 60, 72; cultural heritage collections 36; ElePHãT project (bibliographic metadata) 59–62, 63, 107; glossary 120; museum object 32; National Statement on Ethical Conduct in Human Research regarding 43; unreproducible data and 45; unstructured data and 46 Metallica v. Napster 42–43 methodology: glossary 120; HASS 44; Linked Data xiii–xiv, 1–2, 9, 32, 36, 37, 93; Linked Open Data 107, 109; mindmap 106 Microcosm 12 Minecraft 52 Misa, T. 11 MODS see Bibliographic Metadata Ontology monetisation 27 Moore, K. 29 motif 1, 23, 75–76, 80; glossary 120 Musk, Elon 44 MySQL see Open-Source Relational Database Management System Napster 42–42 Narayen, Shantanu 83n7 National Statement on Ethical Conduct in Human Research 43 NEPOMUK see Networked Environment for Personalized, Ontology-Based Management of Unified Knowledge (NEPOMUK) Contact Ontology Networked Environment for Personalized, Ontology-Based Management of

Unified Knowledge (NEPOMUK) Contact Ontology 74 Networked Knowledge Organization Systems (NKOS) 36 NKOS see Networked Knowledge Organization Systems Nomisma.org project 98, 110 non-proprietary: data 53; format 95; glossary 120; software 53 Nurmikko-Fuller, Terhi 17, 25, 44, 90, 100n5, 106, 110; see also Burrows; Pickering Object see Subject and Object objects: collecting 89; digital 52; inanimate 86; material 32, 43, 46; multimedia 45; museum 57, 77–79 OCR see Optical Character Recognition O’Hara, K. 29 oligopolies 23, 107 O’Malley, Katie 34 Online Jazz Discography (J-DISC) 95 online oligopolies see oligopolies ontolog* (ontology, ontologies, ontological) 67–82, 106; bibliographic metadata 60; Beecher 67; bias in 69–71; “borrowing” of term from philosophy 17; classes and properties of 68; definition of 15, 67–70; development and developers 116–117; DuCharme 68; FRBRoo 45; glossary 120; Gruber 15, 67; Hyvönen 67; mapping 49; Linked Data 113–114; modelling 73, 95; Music Ontology 95; representation 69, 77–81; Semantic Web and 67; see also Functional Requirements for Bibliographic Records; International Council of Museums (ICOM)’s Conceptual Reference Model; MADS; MODS; NEPOMUK; OntoMedia ontology; OWL; PROVO-O ontological structures 16–18, 46, 60; data-driving 100; underlying 79 OntoMedia ontology 18, 77, 79–80 Open Access 51, 52 Open Data 51, 53–54; see also Linked Open Data openness 50–56 Open Source 51, 52 Open-Source Relational Database Management System (MySQL) 93

Index Optical Character Recognition (OCR) 49 Orwell, George 24–27 OWL see Web Ontology Language Pacific And Regional Archive for Digital Sources in Endangered Cultures (PARADISEC) 58 Panopticon 27 PARADISEC see Pacific And Regional Archive for Digital Sources in Endangered Cultures Pariser, Eli 31 Parker, Trey 42 Patfield, S. 6 PDF see Portable Document Format Peroni, Silvio 18 perspective (s): blinkered 92; business model 51; contemporary cultural 73; data privacy 35; different 4, 36, 59; diverse 74; inherent 115; Linked Data 82, 107; Marxist 105; modern 76; musicology 95; technological 13, 117; Western 70, 81 Pichai, Sundar 83n7 Pickering, P. 17, 25, 100n5, 106 PokemonGo! 24, 52 Portable Document Format (PDF) 49, 53, 95 PR see public relations privacy 19, 23–38, 56; case for 58; data management and 98; data publication and concerns regarding 57; death of 23; Linked Data as potential catastrophe for 26, 36–37, 107, 116; as right not privilege 26–28; see also anonymisation Privacy Paradox 28–30, 35, 108 profiles, social media 25, 27, 29, 35 profiling: data 25–26, 31, 35, 116 Provenance Ontology (PROV-O) 71 PROV-O see Provenance Ontology Pubby 98 public relations (PR) 59 QS see Quantified Self Quantified Self (QS) 33 Query Builder with Natural Language Processing (SPARKLIS) 98 RDBMS see relational database management system RDF see Resource Description Framework

127

RDFS see Resource Description Framework Schema RDF/XML 93, 110; glossary 120 Reddit 26, 44 Reeves, T. 3 relational database management system (RDBMS) 93 Repko, A. 3 ResearchGate 44 Resource Description Framework (RDF): data converted to 60–62; Five Star Linked Data and 115; graph structure 94; instance-level 91; Linked Data and 110; ontologies as 68, 114; MADS-RDF ontology 72; metadata 93; RBDMS as 93; RDF/XML 93, 110, 120; “simple” process to produce 99; W3C standards 55–56 Resource Description Framework Scheme (RDFS) 69, 71; examples of 112 Resource Description Framework (RDF) triple 13–14, 15–16, 19, 32; Body & Soul workflow 100; inherent flexibility and robustness of 106; jazz recording 95, 96–98; querying 61; semantic queries 111; skills to create 105; SPARQL and 113; storing 93–94; Terse RDF Triple Language 96, 110 Rodin, Auguste 1–2, 18 Russia 24, 54 Sacks, Oliver 87 Sarandos, Ted 70 Schöch, C. 46 Science, Technology, Engineering, Mathematics (STEM) 2–11, 19, 116 Science, Technology, Engineering, Arts, Mathematics (STEAM) 4, 20n3 scraping (of data) 26–27, 44 Semantic Lab, Pratt 97 semantic models 15 semantics 113; embedded 111 semantic shift 36; glossary 120 Semantic Web 16, 18, 32, 54–44, 82, 103, 117; Foucault and 16–17; ontological modelling for 70; ontologies as crucial to 67; OWL as tool and language of 14, 69; RDFS as language of 69; see also ElePHãT project Shakespeare, William 6, 13–15

128

Index

SIG see Special Interest Group Silicon Valley 70, 81 Simpson, R. 16, 89 Smith, David 57 Snow, C.: Two Cultures theory of 5–10, 19 social feminist theory 5 social engagement 31 social judgement 80 social media 23–25, 27–29, 35–37, 92; Linked Data and 108; as coping mechanism 29; data 44; see also Facebook; Instagram; influencers; profiles, social media; Reddit; TikTok; Twitter social networking 34 SPARKLIS see Query Builder with Natural Language Processing SPARQL (SPARQL RDF and Query Language) 12, 118n10; endpoint 61; queries 61, 100, 106, 110, 112–113; querying across three datasets using 97–98; SQL compared to 93 SPARQL Protocol 55, 97 Special Interest Group (SIG) 88 Spotify 43, 52 SQL see Structured Query Language SQLite3 96; glossary 120 Stallman, Richard 51 Steiner, Peter 23 STEM see Science, Technology, Engineering, Mathematics STEAM see Science, Technology, Engineering, Arts, Mathematics Steam platform 52 Stone, Matt 42 Strava 33–36 Structured Query Language (SQL) 49, 98, 118; SQLite3 96, 120 Suárez, J. 2 Subject see Domain Subject and Object 13, 17, 110 subject domain 69 Subject-Predicate-Object 13, 110 Subject-Verb-Object (SVO) 13 Sumerian: glossary 120 Sumerian literature see Electronic Text Corpus of Sumerian Literature surveillance capitalism 27, 31 surveillance technology 24–25, 29, 35–36 SVO see Subject-Verb-Object syntax 93, 97, 110, 112; glossary 121 Szostak, R. 3

tabular data 46, 47, 49, 91, 93–94; Body and Soul discography 95–96; glossary 121; JazzCats 97, 99; as knowledge structure 98; Web Karma 96 tubular database 100 tabular dataset 12, 53 tech utopianism 53, 57, 100, 107–108, 115; glossary 121 Terse RDF Triple Language (TTL) 96, 110 Three Wise Monkeys 38 Trump, Donald 26 Thune, [John] 27 TikTok 30–31, 44 triplestore 89, 93–94, 99–100, 105, 110–111, 114–116; glossary 121; JazzCat 97 Truce Village, Korea 24 trust 23–38, 56, 98 TTL see Terse RDF Triple Language Turtle syntax 97, 110, 112 Twitter and tweeting 26, 30, 44, 92 Ulrich, Lars 42 Uniform Resource Identifier (URI) xvii, 14 Uniform Resource Locator (URL) xvii, 14 Uniform Resource Number (URN) xvii, 14 United States Senate hearings: Facebook data privacy 27; Metallica v. Napster 42 URI see Uniform Resource Identifier URL see Uniform Resource Locator URN see Uniform Resource Number utopian ideal see tech utopianism vagueness 18, 67, 104 Verborgh, R. xiii, 91, 94 VIAF see Virtual International Authority File Virtual International Authority File (VIAF) 12, 15 Vkontakte 24 W3C see World Wide Web Consortium (W3C) standard web see World Wide Web Consortium (W3C) standard web crawling or scraping 44 Web of Data 16, 18, 109 Web of Documents 18 Web Karma software 96

Index Web Ontology Language (OWL) 69 Web Science 4 WhatsApp 35 What You See Is What You Get (WYSIWYG) 106, 117n6 Wiederhold, B. 29 Wilks, Y. 16, 67 Wisdom, House of 10 Wisdom Literature 80; glossary 121 Wittgenstein, L. 13–16 WordPress 88 workflows 12, 31, 46, 60; designing 91; glossary 121; JazzCat 99–100; Linked Data 91, 108, 110, 114–116; PARADISEC 58; RDF 94–97; research 54; Weimar Jazz 96, 100

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World Wide Web Consortium (W3C) standard xiv, 12, 32, 55, 105, 109, 115 WYSIWYG see What You See Is What You Get XML see Extensible MarkUp Language (XML) XML Path Language 93 Xpath see XML Path Language Xquery 93; glossary 121 XSLT see Extensible Stylesheet Language Transformations Zeng, Marcia Lei 47 Zuboff, S. 27, 29, 31 Zuckerberg, Mark 27, 70