Semantic Technologies for Intelligent Industry 4.0 Applications

Cover
Half Title
Series
Title
Copyright
Contents
Preface
List of Contributors
List of Figures
List of Tables
List of Abbreviations
1 Semantic Search Engine in Industry 4.0
1.1 Introduction
1.2 Information Retrieval
1.3 Search Engine
1.3.1 Traditional Search engine
1.3.2 Semantic search engine
1.3.3 Approaches and categorization of semantic search
1.4 Semantic Search Engine in Industry 4.0
1.4.1 Industry 4.0
1.4.2 Role of semantic search in industry 4.0
1.4.2.1 Search engines for Internet of Things (IoT)
1.4.2.2 Search engines for internet of services (IoS)
1.4.2.3 Search engines for big data
1.5 Conclusion
2 Semantic Web Services: The Interoperable Middleware Technology for Industry 4.0
2.1 Introduction
2.2 Semantic Web Services
2.2.1 Concepts of [web] service
2.2.2 Web services
2.2.3 Semantic web and web services
2.3 Challenges and Prospects of Industry 4.0
2.3.1 Challenges of industry 4.0
2.3.2 Prospects of industry 4.0
2.4 Conclusion
3 Semantic Web of Things for Healthcare Interoperability using IoMT Technologies
3.1 Introduction
3.1.1 Overview of industrial internet of things (IIoT)
3.1.2 Requirements of SWT for medical devices
3.1.3 Semantic interoperable healthcare industry using IoT system
3.2 Related Works
3.3 Network architecture of SWT for healthcare
3.4 Methodology
3.4.1 Proposed semantic web technologies of interoperability using IoT
3.4.2 Ontology validation tools
3.4.3 Biomedical ontology domain
3.4.4 Security and privacy concerns of semantic web of IoMT
3.5 Implementation of Knowledge-driven Framework in TIMER
3.5.1 Temporal information modeling, extraction, and reasoning (TIMER)
3.5.2 Clinical narrative temporal relation ontology (CNTRO)
3.5.3 Semantic ontology-driven translator
3.5.4 Semantic knowledge representation ontology
3.5.5 Connectivity management semantics ontology (CMTS)
3.6 Experimental Analysis
3.6.1 Reasoning for healthcare Ocontext-rule-based decision support ontology
3.6.2 Evaluation of ontology modeling for IoMT services
3.6.3 Hierarchical semantic information modeling ontology structure
3.7 Semantic Industry for Applications
3.7.1 Applications of smart health semantic industry
3.7.2 Semantic web technologies in e-healthcare
3.7.3 IoT e-health ontologies framework
3.7.4 Semantic interoperability in IoT applications
3.8 Limitations and Challenges
3.9 Conclusions and Future Enhancements
4 AI Compatible Key Hardware Design for Smart Warehouse: A Practical Implementation
4.1 Introduction
4.2 System Description
4.3 Key Hardware Design
4.3.1 Telescopic fork
4.3.1.1 First version approach
4.3.1.2 Improved version of the design
4.3.2 Controller design
4.3.3 Data collection
4.4 Results and Discussion
4.4.1 Telescopic fork
4.4.2 Controller
4.4.3 Data collection
4.5 Conclusion
5 A Know ledge Graph-based Integration Approach for Research Digital Artifacts
5.1 Introduction
5.1.1 Motivation
5.1.2 Contribution
5.2 Related Work
5.3 Method
5.3.1 Methodology
5.3.2 Research Digital Artifact Knowledge Graph
5.4 Result
5.4.1 Dataset
5.4.2 Schema
5.4.3 Mapping rules
5.4.4 Analysis
5.4.5 Discussion
5.5 Conclusion
6 A Review of Ontology Development Methodologies: The Way Forward for Robust Ontology Design
6.1 Introduction
6.2 Ontology Development
6.3 The Existing Ontology Development Methodology: The Review
6.3.1 Gruninger and fox's methodology
6.3.2 Methontology methodology
6.3.3 Noy—McGuiness methodology
6.3.4 Uschold—King methodology
6.4 Way Forward for Robust Ontology Design: The Review
6.5 Proposed Methodology: Determinants for Robust Ontology Design
6.6 Discussion and Conclusion
7 Semantic Web: An Overview and a .net-based Tool for Knowledge Extraction and Ontology Development
7.1 Introduction
7.1.1 Semantic web
7.1.2 Ontology
7.1.3 Ontology languages
7.1.3.1 Rule languages
7.1.4 Ontology learning
7.1.5 Ontology editor
7.1.5.1 Ontology editing tools
7.1.5.2 Ontology editing in .net platforms
7.1.5.3 The need for a .net-based ontology editor
7.2 A Tool for Ontology Editing in .NET Platform
7.3 Implementation Details
7.3.1 Ontology editor
7.3.2 Visnalizer
7.3.2.1 Querying interface/reasoning
7.3.2.2 Knowledge extraction interface
7.3.2.3 Ontology development methodology
7.4 Ontology Development in TODE
7.5 Conclusion
8 AedesOnt: Ontology for Aedes Mosquito Vectors to Predict Semantic Relations of Biocontrol Agents
8.1 Introduction
8.2 Aedes Mosquito Vector
8.2.1 Aedes life cycle
8.2.2 Insecticide resistance behavior
8.2.3 Aedes mosquito valiants
8.3 Vector Control Techniques
8.3.1 Environmental control
8.3.2 Chemical control
8.3.3 Genetic and immunological control
8.3.4 Biological control
8.4 Role of Ontologies in Vector Control
8.4.1 Existing ontologies for vector control
8.4.2 Need of ontology for aedes mosquito
8.5 Aedes Mosquito Vector Ontology
8.5.1 Aedes ontology development
8.6 Results and Discussion
8.7 Conclusion
9 Paradigms for Integration of Biomedical Knowledge with Patients’ Records: Brief Trajectory and Roles of Ontology
9.1 Introduction
9.2 Methods
9.3 Results
9.3.1 Knowledge inscription
9.3.2 Knowledge catalog
9.3.3 Knowledge agent
9.3.4 Expert systems
9.3.5 Knowledge modeled as an ontology
9.4 Discussion
9.4.1 Summary of literature review results
9.4.2 Interpretation of the literature review results
9.4.3 Limitations
9.5 Conclusion
10 Semantic Checking of Information Support for Heterogeneous Resources of Train Speed Restrictions by Ontological Means
10.1 Introduction
10.2 Problem Statement and Purpose
10.3 Related Works
10.3.1 Ontological modelling in transport, taking into account defects and speed restrictions
10.3.2 Ontological modelling of computer, medical, and construction domains, taking into account defects
10.3.3 Application of the relations composition in ontology development
10.4 Modular Railway Track Defect Ontology
10.5 Implementation of Railway Track Defect Ontology
10.5.1 Resources ontology
10.5.2 Railway track defect ontology
10.6 Speed Restriction Checking
10.7 Discussion
10.8 Conclusions
11 A Tool for Automatic Anomaly Identification in OWL Ontologies
11.1 Introduction
11.2 Related Work
11.3 ONTO-Analyst System
11.3.1 First stage
11.3.2 Second stage
11.3.3 Third stage
11.4 Anomalies to be Identified
11.4.1 Exact circularity in taxonomy
11.4.2 Circular properties
11.4.3 Circularity between rules and taxonomy
11.4.4 Partition error in taxonomy
11.4.5 Multiple functional properties
11.4.6 Contradicting rules
11.4.7 Incompatible rule antecedent
11.4.8 Self-contradicting rule
11.4.9 Redundancy by repetitive taxonomic definition
11.4.10 Redundant cardinalities
11.4.11 Redundant implication
11.4.12 Redundant implication of transitivity or symmetry
11.4.13 Redundant use of transitivity and symmetry
11.4.14 Redundant derivation in the antecedent
11.4.15 Rule subsumption
11.4.16 Chains of inheritance
11.4.17 Lonely disjoint classes
11.4.18 Lazy class or property
11.5 Experiments
11.5.1 Ontology repository selection
11.5.2 Ontologies download
11.5.3 Conversion to MetaFOR format
11.5.3.1 Data summarv of the structures of the analyzed ontologies
11.5.4 Identification and analysis of the anomalies
11.5.4.1 General overview
11.5.4.2 Analysis of some specific anomalies
11.5.4.3 Most relevant ontologies
11.6 Discussion
11.6.1 Too many anomalies?
11.6.2 Anomaly detection
11.6.3 Rules are not used in ontologies
11.6.4 Top 10 bioportal ontologies
11.7 Conclusion
12 Ontological Modeling for the Personalization of Learning Environment of the University
12.1 Introduction
12.2 Application of an Ontological Approach to Managing the Process
12.2.1 Application of Ontologies to Represent Knowledge About the Study of Cognitive Functions
12.2.2 Ontologies as a Mechanism for Implementing a Personalized Approach in Professional Activities
12.3 Formal Ontological Model for Managing the Process
12.4 Implementation of Ontology Models in Protégé 5.5
12.4.1 Learner Ontology
12.4.2 Educational Content Ontology
12.4.3 Cognitive Function Ontology
12.5 Methodological Basis for Building a Personalized Digital Educational
12.6 Conclusion
Index
About the Editors