Semantic technologies for intelligent industry 4. 0 applications / edited by Archana Patel and Narayan C. Debnath.

Format:

Book

Contributor:

Paṭela, Arcanā, editor.

Debnath, N. C. (Narayan C.), editor.

Series:

River Publishers Series in Computing and Information Science and Technology Series

Language:

English

Subjects (All):

Industry 4.0.

Semantic computing.

Physical Description:

1 online resource (395 pages)

Place of Publication:

Gistrup, Denmark : River Publishers, [2023]

Summary:

This book aims to provide a roadmap for semantic technologies and highlights the role of these technologies in industry, and thus will serve as an important guide towards the latest industrial applications of semantic technologies for the upcoming generation.

Contents:

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.

Notes:

Includes index.

Description based on print version record.

ISBN:

1-5231-5635-X

1-00-344113-0

1-003-44113-0

1-000-96410-8

87-7022-781-0

9781003441137

OCLC:

1393244756