Semantic Web. Technologies Research and Applications 2022017870, 2022017871, 9781032313696, 9781003309420
549 112 14MB
English Pages [405] Year 2023
Table of contents :
Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Notes on Editors
List of Contributors
Preface
Chapter 1 Semantic Web Technologies
1.1 Introduction
1.2 The Concept of Semantic Web
1.3 Semantic Web Technologies
1.3.1 Semantic Web Standards
1.3.1.1 URI/IRI
1.3.1.2 XML
1.3.1.3 RDF/RDFS
1.3.1.4 SKOS
1.3.1.5 OWL
1.3.1.6 SPARQL
1.3.1.7 RIF
1.3.2 Semantic Web Methods
1.3.2.1 Contextual Analysis
1.3.2.2 Reasoning Engine
1.3.2.3 Natural Language Understanding
1.3.2.4 Knowledge Graph
1.3.2.5 Linked Data
1.3.2.6 Ontology
1.3.3 Semantic Web Tools
1.3.3.1 Semantic Knowledge Annotation Tools
1.3.3.2 Semantic Knowledge Acquisition Tools
1.3.3.3 Semantic Knowledge Representation Tools
1.3.3.4 Reasoners
1.4 A Peep into the Pragmatics of Semantic Web Technologies
1.5 Conclusion
References
Chapter 2 Leveraging Semantic Web Technologies for Veracity Assessment of Big Biodiversity Data
2.1 Introduction
2.1.1 Motivation and Research Challenges
2.1.2 Research Objectives
2.2 Related Work
2.3 Method
2.3.1 Data Definitions
2.3.2 Data Consistency Analysis
2.3.3 Data Mapping Procedures
2.4 Result
2.4.1 Dataset
2.4.2 Dataset Vocabulary
2.4.3 Data Structure Analysis
2.4.4 Data Type Analysis
2.4.5 Data Granularity Analysis
2.5 Conclusion
Acknowledgement
References
Notes
Chapter 3 Semantic Web Technologies: Latest Industrial Applications
3.1 Introduction
3.2 Business Value of Semantic Web Technology (SWT)
3.2.1 Graph Databases
3.2.2 SWT and Machine Learning
3.2.3 Semantic Models
3.2.4 Agile Data
3.2.5 Data Integrity
3.2.6 W3C Standards
3.2.7 Data Governance
3.2.8 Explainable Inferences
3.2.9 Linked Data
3.2.10 Data Visualization
3.2.11 Data Virtualization
3.2.12 Digital Twins
3.3 The Big Picture: A Data Fabric
3.4 Semantic Web Technology Applications
3.4.1 Semantic Search
3.4.2 Internet of Things (IoT)
3.4.3 Expert Systems and AI
3.4.4 Harmonization
3.4.5 Enterprise Data Models
3.4.6 Enterprise 360
3.4.6.1 Montefiore and Franz Entity-Event Model
3.4.7 Recommendation Engines
3.5 Conclusion
3.5.1 Barriers to Adoption
3.5.2 Suggested Next Steps
3.5.2.1 Avoid Analysis Paralysis
3.5.2.2 Utilize Agile Methods
3.5.2.3 Start with an “Easy Win” First Project with Business Value
3.5.2.4 Expand Your Knowledge of SWT
3.6 Glossary
References
Chapter 4 Latest Applications of Semantic Web Technologies for Service Industry
4.1 Introduction
4.2 Applications of SWTs in Business and Finance
4.2.1 Business Intelligence Applications
4.2.2 Customer Relationship Management Applications
4.2.3 Content Discovery
4.2.4 Collaboration
4.3 Applications of SWTs in Law
4.3.1 Storage of Information in Law
4.3.2 Information Retrieval in Law
4.3.3 Question and Answering in Law
4.3.4 Summarization in Law
4.4 Applications of SWTs in Health
4.4.1 SWTs and Standardization/Interoperability in Health
4.4.2 SWTs and Clinical Information Management
4.4.3 SWTs and Precision Medicine
4.5 Applications of SWTs in Security
4.5.1 Application of SWTs to Cyber-security
4.5.2 Application of SWTs to Counter-Terrorism
4.5.3 Application of SWTs to Policing
4.6 Applications of SWTs in Education
4.6.1 SWTs and Resource Retrieval
4.6.2 SWTs and Resource Recommendation
4.6.3 SWT and Adaptive e-Learning
4.7 Applications of SWTs in Research and Development
4.7.1 SWTs and Knowledge Production
4.7.2 SWTs and Knowledge Accumulation
4.7.3 SWTs and Knowledge Application
4.8 Applications of SWTs in Communication
4.8.1 SWTs and Telecommunication Service Delivery
4.8.2 SWTs and Social Media Platforms
4.9 Applications of SWTs in Hospitality
4.9.1 SWTs and Guest Attraction
4.9.2 SWTs and Guest Retention
4.10 Applications of SWTs in Utility
4.11 Applications of SWTs in Governance
4.12 Applications of SWTs in Logistics and Transportation
4.12.1 SWTs and Land L&T Industry
4.12.2 SWTs and Air L&T Industry
4.12.3 SWTs and Water L&T Industry
4.13 Applications of SWTs in Meteorology
4.14 Summary and Conclusion
References
Notes
Chapter 5 Semantic Web Ontology Centred University Course Recommendation Scheme
5.1 E-learning
5.1.1 E-learning during Covid19
5.2 Recommender Systems
5.2.1 Recommendation Systems in the E-learning Domain
5.3 Ontology
5.3.1 Applications of Ontology
5.3.1.1 Ontologies in E-learning
5.3.2 Ontology-Based Recommender Systems in E-learning
5.3.3 Case Study
5.4 Performance Analysis
5.5 Conclusion
References
Chapter 6 Exploring Reasoning for Utilizing the Full Potential of Semantic Web
6.1 Introduction
6.1.1 Semantics in Semantic Web
6.1.2 Semantic Web Architecture
6.2 Ontologies
6.2.1 Role of Ontologies in Semantic Web
6.3 Web Ontology Language (OWL)
6.3.1 DL Reasoners
6.4 Rules in Semantic Web
6.4.1 Need for Rules
6.4.2 Limitations of Ontology Formalisms
6.4.3 Ontologies and Rules
6.4.4 Importance of Rules
6.4.5 Rules-Based Reasoning
6.4.5.1 Types of Rules
6.4.5.2 Rule Languages
6.4.5.3 Rule Engine
6.5 Conclusion
References
Chapter 7 Ontology Modeling: An Overview of Semantic Web Ontology Formalisms and Engineering Approaches with Editorial Tools
7.1 Introduction
7.2 Ontology Modeling Methodology
7.2.1 SAMOD
7.2.2 TOVE
7.2.3 Pattern-Based
7.2.4 Methontology
7.2.5 Lexicon-Based
7.2.6 KACTUS
7.2.7 Horrocks Method
7.2.8 Developing Ontology-Grounded Methods and Applications(DOGMA)
7.2.9 Fuzzy Ontology Development Method
7.3 Ontology Modeling Formalisms
7.3.1 Ontology Languages
7.3.1.1 XML
7.3.1.2 DAML and OIL
7.3.1.3 RDF+RDFS
7.3.1.4 KIF
7.3.1.5 RIF
7.3.1.6 SADL
7.3.1.7 OWL
7.3.2 Semantic Query and Rule Languages
7.3.2.1 Query Languages
7.3.2.2 SWRL
7.3.2.3 SQWRL
7.3.2.4 Semantic Web Reasoners and Rule Engines
7.4 Ontology Modeling Tools
7.4.1 SWOOP
7.4.2 Apollo
7.4.3 OntoEdit
7.4.4 KAON
7.4.5 WebOnto
7.4.6 OntoStudio
7.4.7 Ontolingua
7.4.8 RDFedit
7.4.9 SWIDE
7.4.10 DODDLE-OWL
7.4.11 TopBraid Composer
7.4.12 IODT
7.4.13 LinkFactory Workbench
7.4.14 Protégé
7.5 Conclusion
References
Chapter 8 Semantic Annotation of Objects of Interest in Digitized Herbarium Specimens for Fine-Grained Object Classification
8.1 Introduction
8.1.1 Annotation of Digitized Herbarium Specimen
8.1.2 Motivation
8.2 Related Work
8.2.1 Ontology for Biodiversity Research
8.2.2 Semantic Annotation for Biodiversity Research
8.2.3 Contributions
8.3 Method
8.3.1 Methodology
8.3.2 Schema Development
8.3.2.1 Entities
8.3.2.2 Entity Relationships
8.3.3 Mapping Rules
8.4 Result
8.4.1 Dataset
8.4.2 Schema
8.4.3 Data Mapping
8.4.4 Discussion
8.5 Conclusion
Acknowledgment
References
Notes
Chapter 9 UpOnto: Strategic Conceptual Ontology Modeling for Unit Operations in Chemical Industries and Their Retrieval Using Firefly Algorithm
9.1 Introduction
9.2 Related Works
9.3 Methodology
9.4 Implementation
9.4.1 Description of Processes in XML
9.4.2 Description of Objects
9.4.3 RDF/OWL Classification of the Reaction Hierarchy
9.4.4 Ontology Modeling using Protégé
9.4.5 Ontology Visualization
9.5 Results and Evaluation
9.5.1 Semiotic Evaluation
9.5.2 Retrieval of the Ontology
9.5.3 Retrieval Evaluation
9.6 Conclusion
References
Chapter 10 Ontologies for Knowledge Representation: Tools and Techniques for Building Ontologies
10.1 Introduction
10.2 Ontology Representation Languages
10.3 Types of Ontologies
10.3.1 General-Purpose Ontologies
10.3.2 Domain-Specific Ontologies
10.4 Techniques for Building Ontologies
10.4.1 Constructing Ontologies from Scratch
10.4.1.1 Case Study on Building Library Ontology
10.4.2 Automatic Construction of Ontologies
10.4.3 Reusability Approach to Ontology Construction
10.4.3.1 Case Study on Building College Management System Ontology (CMS)
10.4.4 Agile Methodology for Ontology Development
10.5 Ontology Editors and Visualization Tools
10.5.1 Ontolingua Server
10.5.2 Protégé
10.5.3 Swoop
10.5.4 OntoEdit
10.5.5 Apollo
10.5.6 OntoViz
10.5.7 WebVOWL
10.5.8 BioOntoVis
10.6 Applications of Ontology and Semantic Web Technologies
10.7 Ontologies for Knowledge Representation: Opportunities and Challenges
10.8 Conclusion
References
Chapter 11 Data Science and Ontologies: An Exploratory Study
11.1 Introduction
11.2 Ontologies
11.3 Role of Ontologies in Data Science
11.3.1 Semantic Data Modelling
11.3.2 Semantic Data Integration
11.3.3 Semantic Data Mining
11.4 Data Science Ontologies
11.5 Related Work
11.6 Conclusion
References
Chapter 12 Ontology Application to Constructing the GMDH-Based Inductive Modeling Tools
12.1 Introduction
12.2 Ontology as the Knowledge Structure of a Domain
12.3 GMDH-based Inductive Modeling as a Technology of Transition from Statistical Data to Mathematical Models
12.3.1 Basic Principles of GMDH
12.3.2 Formal Statement of the GMDH Problem
12.3.3 Kinds of Problems and Software Implementations of GMDH Algorithms
12.4 Structuring the GMDH Domain Knowledge to Design the Ontology Models
12.4.1 Diagram of Sequential Decision Making in the Process of Inductive Modeling
12.4.2 Definition of Basic Concepts of the GMDH Domain
12.4.3 Identifying Relationships between Concepts
12.4.4 Determining Key Parameters of Main Stages of the Modeling Process
12.5 Designing GMDH Tools Using Ontological Models of the Domain
12.5.1 Requirements for a Set of Tools
12.5.2 Ontological Principles of User Interface Construction
12.5.3 Structure of the Software Complex
12.5.4 Main Requirements for the Intelligent User Interface
12.6 Ontology Models of the Domain for Designing Generalized GMDH Tool
12.7 Conclusion
References
Chapter 13 Exploring the Contemporary Area of Ontology Research: FAIR Ontology
13.1 Introduction
13.2 FAIR Ontology and FAIRification
13.3 Role of Ontology Libraries in FAIR Ontologies
13.4 FAIR Ontologies: Research Paradigms
13.5 Conclusion
References
Chapter 14 Analysis of Ontology-Based Semantic Association Rule Mining
14.1 Introduction
14.2 Association Rule Mining
14.2.1 Interestingness Measure
14.2.2 Rules Generation
14.2.3 Applications
14.3 Semantic Association Rule Mining (SARM)
14.3.1 Semantic Proxies
14.3.2 Semantic Association Approaches
14.4 Ontology-Based Semantic Association Rule Mining (OSARM)
14.4.1 Framework
14.4.2 Applications
14.5 Discussion
14.6 Conclusion
References
Chapter 15 Visualizing Chat-Bot Knowledge Graph Using RDF
15.1 Introduction
15.1.1 Semantic Web
15.1.2 Chatbots
15.1.3 Paper’s Contribution
15.2 Related Work
15.3 Proposed Work
15.3.1 Query Preprocessing & NLP
15.3.2 Query Type Detection
15.3.3 Bot Modules
15.3.4 Context Awareness Engine
15.4 Implementation Details
15.4.1 Tools/Technologies used
15.4.2 Steps of Implementation
15.5 Results
15.6 Conclusion
References
Chapter 16 Toward Data Integration in the Era of Big Data: Role of Ontologies
16.1 Introduction and Motivation
16.2 Background
16.2.1 Big Data Characteristics
16.2.2 Big Data Integration
16.2.3 NoSQL Data Bases
16.2.4 Ontologies
16.3 Related Works
16.4 Proposed Approach
16.5 Conclusion
References
Notes
Index
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