Semantic technologies for intelligent industry 4.0 applications
As the world enters the era of big data, there is a serious need to give a semantic perspective to the data in order to find unseen patterns, derive meaningful information, and make intelligent decisions. Semantic technologies offer the richest machine-interpretable (rather than just machine-process...
Saved in:
| Other Authors: | , |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
[Place of publication not identified] :
River Publishers,
2023.
|
| Series: | River Publishers series in computing and information science and technology technology.
|
| Subjects: | |
| ISBN: | 9788770227810 8770227810 9781000964103 1000964108 9781003441137 1003441130 9781000964134 1000964132 8770227829 9788770227827 |
| Physical Description: | 1 online resource (394 pages). |
Cover
Table of contents
| LEADER | 13246cam a2200505 i 4500 | ||
|---|---|---|---|
| 001 | kn-on1393244756 | ||
| 003 | OCoLC | ||
| 005 | 20240717213016.0 | ||
| 006 | m o d | ||
| 007 | cr cn||||||||| | ||
| 008 | 230811s2023 xx o 001 0 eng d | ||
| 040 | |a IEEEE |b eng |e rda |e pn |c IEEEE |d EBLCP |d YDX |d TYFRS |d UKMGB |d OCLCO |d UKAHL |d OCLCF | ||
| 020 | |a 9788770227810 |q (electronic bk.) | ||
| 020 | |a 8770227810 |q (electronic bk.) | ||
| 020 | |a 9781000964103 |q (electronic bk.) | ||
| 020 | |a 1000964108 |q (electronic bk.) | ||
| 020 | |a 9781003441137 |q (electronic bk.) | ||
| 020 | |a 1003441130 |q (electronic bk.) | ||
| 020 | |a 9781000964134 |q (electronic bk. : EPUB) | ||
| 020 | |a 1000964132 |q (electronic bk. : EPUB) | ||
| 020 | |z 8770227829 | ||
| 020 | |z 9788770227827 | ||
| 024 | 7 | |a 10.1201/9781003441137 |2 doi | |
| 035 | |a (OCoLC)1393244756 |z (OCoLC)1393345137 | ||
| 245 | 0 | 0 | |a Semantic technologies for intelligent industry 4.0 applications / |c editors, Archana Patel, Narayan C. Debnath. |
| 264 | 1 | |a [Place of publication not identified] : |b River Publishers, |c 2023. | |
| 300 | |a 1 online resource (394 pages). | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a River Publishers series in computing and information science and technology technology | |
| 506 | |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty | ||
| 520 | |a As the world enters the era of big data, there is a serious need to give a semantic perspective to the data in order to find unseen patterns, derive meaningful information, and make intelligent decisions. Semantic technologies offer the richest machine-interpretable (rather than just machine-processable) and explicit semantics that are being extensively used in various domains and industries. These technologies reduce the problem of large semantic loss in the process of modelling knowledge, and provide sharable, reusable knowledge, and a common understanding of the knowledge. As a result, the interoperability and interconnectivity of the model make it priceless for addressing the issues of querying data. These technologies work with the concepts and relations that are very close to the working of the human brain. They provide a semantic representation of any data format: unstructured or semi-structured. As a consequence, data becomes real-world entity rather than a string of characters. For these reasons, semantic technologies are highly valuable tools to simplify the existing problems of the industry leading to new opportunities. However, there are some challenges that need to be addressed to make industrial applications and machines smarter. This book aims to provide a roadmap for semantic technologies and highlights the role of these technologies in industry. The book also explores the present and future prospects of these semantic technologies along with providing answers to various questions like: Are semantic technologies useful for the next era (industry 4.0)? Why are semantic technologies so popular and extensively used in the industry? Can semantic technologies make intelligent industrial applications? Which type of problem requires the immediate attention of researchers? Why are semantic technologies very helpful in people's future lives? This book will potentially serve as an important guide towards the latest industrial applications of semantic technologies for the upcoming generation, and thus becomes a unique resource for scholars, researchers, professionals and practitioners in the field. | ||
| 505 | 0 | |a Preface xiii List of Contributors xv List of Figures xix List of Tables xxv List of Abbreviations xxvii 1 Semantic Search Engine in Industry 4.0 1 1.1 Introduction 2 1.2 Information Retrieval 3 1.3 Search Engine 8 1.3.1 Traditional Search engine 8 1.3.2 Semantic search engine 11 1.3.3 Approaches and categorization of semantic search 11 1.4 Semantic Search Engine in Industry 4.0 15 1.4.1 Industry 4.0 15 1.4.2 Role of semantic search in industry 4.0 16 1.4.2.1 Search engines for Internet of Things (IoT) 16 1.4.2.2 Search engines for internet of services (IoS) 17 1.4.2.3 Search engines for big data 18 1.5 Conclusion 19 2 SemanticWeb Services: The Interoperable Middleware Technology for Industry 4.0 25 2.1 Introduction 26 2.2 Semantic Web Services 31 2.2.1 Concepts of [web] service 32 2.2.2 Web services 32 2.2.3 Semantic web and web services 34 2.3 Challenges and Prospects of Industry 4.0 39 2.3.1 Challenges of industry 4.0 39 2.3.2 Prospects of industry 4.0 41 2.4 Conclusion 43 3 Semantic Web of Things for Healthcare Interoperability using IoMT Technologies 49 3.1 Introduction 50 3.1.1 Overview of industrial internet of things (IIoT) 51 3.1.2 Requirements of SWT for medical devices 53 3.1.3 Semantic interoperable healthcare industry using IoT system 54 3.2 Related Works 56 3.3 Network architecture of SWT for healthcare 57 3.4 Methodology 58 3.4.1 Proposed semantic web technologies of interoperability using IoT 58 3.4.2 Ontology validation tools 59 3.4.3 Biomedical ontology domain 60 3.4.4 Security and privacy concerns of semantic web of IoMT 61 3.5 Implementation of Knowledge-driven Framework in TIMER 62 3.5.1 Temporal information modeling, extraction, and reasoning (TIMER) 62 3.5.2 Clinical narrative temporal relation ontology (CNTRO) 63 3.5.3 Semantic ontology-driven translator 64 3.5.4 Semantic knowledge representation ontology 65 3.5.5 Connectivity management semantics ontology (CMTS) 65 3.6 Experimental Analysis 66 3.6.1 Reasoning for healthcare 0context-rule-based decision support ontology 66 3.6.2 Evaluation of ontology modeling for IoMT services 67 3.6.3 Hierarchical semantic information modeling ontology structure 69 3.7 Semantic Industry for Applications 71 3.7.1 Applications of smart health semantic industry 71 3.7.2 Semantic web technologies in e-healthcare 73 3.7.3 IoT e-health ontologies framework 75 3.7.4 Semantic interoperability in IoT applications 76 3.8 Limitations and Challenges 77 3.9 Conclusions and Future Enhancements 78 4 AI Compatible Key Hardware Design for SmartWarehouse: A Practical Implementation 83 4.1 Introduction 84 4.2 System Description 87 4.3 Key Hardware Design 89 4.3.1 Telescopic fork 89 4.3.1.1 First version approach 89 4.3.1.2 Improved version of the design 92 4.3.2 Controller design 93 4.3.3 Data collection 95 4.4 Results and Discussion 99 4.4.1 Telescopic fork 99 4.4.2 Controller 100 4.4.3 Data collection 104 4.5 Conclusion 107 5 A Knowledge Graph-based Integration Approach for Research Digital Artifacts 111 5.1 Introduction 112 5.1.1 Motivation 114 5.1.2 Contribution 114 5.2 Related Work 115 5.3 Method 117 5.3.1 Methodology 117 5.3.2 Research Digital Artifact Knowledge Graph 118 5.4 Result 119 5.4.1 Dataset 120 5.4.2 Schema 121 5.4.3 Mapping rules 121 5.4.4 Analysis 125 5.4.5 Discussion 126 5.5 Conclusion 127 6 A Review of Ontology Development Methodologies: The Way Forward for Robust Ontology Design 139 6.1 Introduction 140 6.2 Ontology Development 142 6.3 The Existing Ontology Development Methodology: The Review 143 6.3.1 Gruninger and fox's methodology 145 6.3.2 Methontology methodology 146 6.3.3 Noy−McGuiness methodology 147 6.3.4 Uschold−King methodology 148 6.4 Way Forward for Robust Ontology Design: The Review 148 6.5 Proposed Methodology: Determinants for Robust Ontology Design 153 6.6 Discussion and Conclusion 154 7 Semantic Web: An Overview and a .net-based Tool for Knowledge Extraction and Ontology Development 169 7.1 Introduction 170 7.1.1 Semantic web 171 7.1.2 Ontology 172 7.1.3 Ontology languages 172 7.1.3.1 Rule languages 173 7.1.4 Ontology learning 174 7.1.5 Ontology editor 176 7.1.5.1 Ontology editing tools 177 7.1.5.2 Ontology editing in .net platforms 179 7.1.5.3 The need for a .net-based ontology editor 181 7.2 A Tool for Ontology Editing in .NET Platform 181 7.3 Implementation Details 183 7.3.1 Ontology editor 184 7.3.2 Visualizer 186 7.3.2.1 Querying interface/reasoning 187 7.3.2.2 Knowledge extraction interface 188 7.3.2.3 Ontology development methodology 188 7.4 Ontology Development in TODE 189 7.5 Conclusion 193 8 Aedes Ont: Ontology for Aedes Mosquito Vectors to Predict Semantic Relations of Biocontrol Agents 199 8.1 Introduction 200 8.2 Aedes Mosquito Vector 202 8.2.1 Aedes life cycle 202 8.2.2 Insecticide resistance behavior 203 8.2.3 Aedes mosquito variants 204 8.3 Vector Control Techniques 206 8.3.1 Environmental control 206 8.3.2 Chemical control 207 8.3.3 Genetic and immunological control 208 8.3.4 Biological control 208 8.4 Role of Ontologies in Vector Control 211 8.4.1 Existing ontologies for vector control 211 8.4.2 Need of ontology for aedes mosquito 214 8.5 Aedes Mosquito Vector Ontology 215 8.5.1 Aedes ontology development 215 8.6 Results and Discussion 218 8.7 Conclusion 219 9 Paradigms for Integration of Biomedical Knowledge with Patients' Records: Brief Trajectory and Roles of Ontology 237 9.1 Introduction 238 9.2 Methods 240 9.3 Results 243 9.3.1 Knowledge inscription 244 9.3.2 Knowledge catalog 245 9.3.3 Knowledge agent 246 9.3.4 Expert systems 248 9.3.5 Knowledge modeled as an ontology 251 9.4 Discussion 253 9.4.1 Summary of literature review results 253 9.4.2 Interpretation of the literature review results 253 9.4.3 Limitations 256 9.5 Conclusion 256 10 Semantic Checking of Information Support for Heterogeneous Resources of Train Speed Restrictions by Ontological Means 269 10.1 Introduction 270 10.2 Problem Statement and Purpose 270 10.3 Related Works 271 10.3.1 Ontological modelling in transport, taking into account defects and speed restrictions 272 10.3.2 Ontological modelling of computer, medical, and construction domains, taking into account defects 273 10.3.3 Application of the relations composition in ontology development 273 10.4 Modular Railway Track Defect Ontology 274 10.5 Implementation of Railway Track Defect Ontology 278 10.5.1 Resources ontology 278 10.5.2 Railway track defect ontology 280 10.6 Speed Restriction Checking 286 10.7 Discussion 287 10.8 Conclusions 287 11 A Tool for Automatic Anomaly Identification in OWL Ontologies 291 11.1 Introduction 292 11.2 Related Work 294 11.3 ONTO-Analyst System 296 11.3.1 First stage 296 11.3.2 Second stage 298 11.3.3 Third stage 299 11.4 Anomalies to be Identified 299 11.4.1 Exact circularity in taxonomy 300 11.4.2 Circular properties 300 11.4.3 Circularity between rules and taxonomy 301 11.4.4 Partition error in taxonomy 302 11.4.5 Multiple functional properties 302 11.4.6 Contradicting rules 302 11.4.7 Incompatible rule antecedent 302 11.4.8 Self-contradicting rule 303 11.4.9 Redundancy by repetitive taxonomic definition 303 11.4.10 Redundant cardinalities 304 11.4.11 Redundant implication 304 11.4.12 Redundant implication of transitivity or symmetry 304 11.4.13 Redundant use of transitivity and symmetry 304 11.4.14 Redundant derivation in the antecedent 305 11.4.15 Rule subsumption 305 11.4.16 Chains of inheritance 305 11.4.17 Lonely disjoint classes 306 11.4.18 Lazy class or property 306 11.5 Experiments 307 11.5.1 Ontology repository selection 307 11.5.2 Ontologies download 308 11.5.3 Conversion to MetaFOR format 308 11.5.3.1 Data summary of the structures of the analyzed ontologies 310 11.5.4 Identification and analysis of the anomalies 311 11.5.4.1 General overview 311 11.5.4.2 Analysis of some specific anomalies 314 11.5.4.3 Most relevant ontologies 314 11.6 Discussion 316 11.6.1 Too many anomalies? 316 11.6.2 Anomaly detection 318 11.6.3 Rules are not used in ontologies 323 11.6.4 Top 10 bioportal ontologies 325 11.7 Conclusion 325 12 Ontological Modeling for the Personalization of Learning Environment of the University 329 12.1 Introduction 330 12.2 Application of an Ontological Approach to Managing the Process 331 12.2.1 Application of Ontologies to Represent Knowledge About the Study of Cognitive Functions 332 12.2.2 Ontologies as a Mechanism for Implementing a Personalized Approach in Professional Activities 333 12.3 Formal Ontological Model for Managing the Process 335 | |
| 505 | 0 | |a 12.4 Implementation of Ontology Models in Protégé 5.5 338 12.4.1 Learner Ontology 338 12.4.2 Educational Content Ontology 343 12.4.3 Cognitive Function Ontology 347 12.5 Methodological Basis for Building a Personalized Digital Educational 349 12.6 Conclusion 352 Index 359 About the Editors 361. | |
| 590 | |a Knovel |b Knovel (All titles) | ||
| 650 | 0 | |a Industry 4.0. | |
| 650 | 0 | |a Semantic computing. | |
| 655 | 7 | |a elektronické knihy |7 fd186907 |2 czenas | |
| 655 | 9 | |a electronic books |2 eczenas | |
| 700 | 1 | |a Patel, Archana |c (Lecturer in software engineering), |e editor. | |
| 700 | 1 | |a Debnath, N. C. |q (Narayan C.), |e editor. | |
| 776 | 0 | 8 | |i Print version: |t SEMANTIC TECHNOLOGIES FOR INTELLIGENT INDUSTRY 4.0 APPLICATIONS. |d [Place of publication not identified] : RIVER PUBLISHERS, 2023 |z 8770227829 |w (OCoLC)1375536940 |
| 830 | 0 | |a River Publishers series in computing and information science and technology technology. | |
| 856 | 4 | 0 | |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpSTIIA006/semantic-technologies-for?kpromoter=marc |y Full text |
Save to List
Cite this
Export Record
Email this