Biffl / Sabou Semantic Web Technologies for Intelligent Engineering Applications

1. Auflage 2016
ISBN: 978-3-319-41490-4
Verlag: Springer Nature Switzerland
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 413 Seiten

This is the first book to explore how Semantic Web technologies (SWTs) can be used to create intelligent engineering applications (IEAs).  Technology-specific chapters reflect the state of the art in relevant SWTs and offer guidelines on how they can be applied in multi-disciplinary engineering settings characteristic of engineering production systems. In addition, a selection of case studies from various engineering domains demonstrate how SWTs can be used to create IEAs that enable, for example, defect detection or constraint checking. Part I 'Background and Requirements of Industrie 4.0 for Semantic Web Solutions' provides the background information needed to understand the book and addresses questions concerning the semantic challenges and requirements of Industrie 4.0, and which key SWT capabilities may be suitable for implementing engineering applications. In turn, Part II 'Semantic Web-Enabled Data Integration in Multi-Disciplinary Engineering' focuses on how SWTs can be used for data integration in heterogeneous, multi-disciplinary engineering settings typically encountered in the creation of flexible production systems. Part III 'Creating Intelligent Applications for Multi-Disciplinary Engineering' demonstrates how the integrated engineering data can be used to support the creation of IEAs, while Part IV 'Related and Emerging Trends in the Use of Semantic Web in Engineering' presents an overview of the broader spectrum of approaches that make use of SWTs to support engineering settings. A final chapter then rounds out the book with an assessment of the strengths, weaknesses and compatibilities of SWTs and an outlook on future opportunities for applying SWTs to create IEAs in flexible industrial production systems. This book seeks to build a bridge between two communities: industrial production on one hand and Semantic Web on the other. Accordingly, stakeholders from both communities should find this book useful in their work. Semantic Web researchers will gain a better understanding of the challenges and requirements of the industrial production domain, offering them guidance in the development of new technologies and solutions for this important application area. In turn, engineers and managers from engineering domains will arrive at a firmer grasp of the benefits and limitations of using SWTs, helping them to select and adopt appropriate SWTs more effectively. In addition, researchers and students interested in industrial production-related issues will gain valuable insights into how and to what extent SWTs can help to address those issues. 

About the Editors: Marta Sabou is Senior Researcher at the Vienna University of Technology, where she leads a group of researchers in the area of semantic representation and integration of engineering data in the context of automation systems. She has a broad expertise in several Semantic Web research topics ranging from ontology engineering tasks (ontology creation, mapping, modularization) to the creation of intelligent systems that benefit from semantic information in domains as varied as tourism, climate change or open government. Her current interest is on data integration issues, with a special focus on the domain of industrial automation and Industrie 4.0. Stefan Biffl is Associate Professor of Software Engineering at the Institute of Software Technology and Interactive Systems, Vienna University of Technology. He is the head of the Christian Doppler research laboratory 'Software Engineering Integration for Flexible Automation Systems', which investigates concepts, methods, and tools for improving production systems engineering processes based on better integration of data in multi-disciplinary engineering projects. Stefan Biffl is also a supervisor in the doctoral college on 'Cyber-Physical Production Systems' at the Vienna University of Technology. His current research interests include software and systems engineering, and product and process improvement.
   

Autoren/Hrsg.

Biffl, Stefan

Sabou, Marta

Inhaltsverzeichnis

1;Foreword I;5
2;Foreword II;7
3;Preface;11
4;Contents;13
5;Contributors;15
6;Abbreviations;17
7;1 Introduction;21
7.1;Abstract;21
7.2;1.1 Context and Aims of This Book;21
7.3;1.2 Industrial Production Systems;24
7.4;1.3 Intelligent Engineering Applications for Industrie 4.0;27
7.5;1.4 Who Should Read This Book and Why?;31
7.6;1.5 Book Content and Structure;31
7.7;Acknowledgments;32
7.8;References;33
8;Background and Requirements of Industrie 4.0 for Semantic Web Solutions;34
9;2 Multi-Disciplinary Engineering for Industrie 4.0: Semantic Challenges and Needs;35
9.1;Abstract;35
9.2;2.1 Introduction;36
9.3;2.2 Production Systems Life Cycle;38
9.4;2.3 Engineering of Industrial Production Systems;43
9.5;2.4 Usage Scenarios that Illustrate Needs for Semantic Support;50
9.5.1;2.4.1 Scenario 1—Discipline-Crossing Engineering Tool Networks;52
9.5.2;2.4.2 Scenario 2—Use of Existing Artifacts for Plant Engineering;54
9.5.3;2.4.3 Scenario 3—Flexible Production System Organization;58
9.5.4;2.4.4 Scenario 4—Maintenance and Replacement Engineering;60
9.6;2.5 Needs for Semantic Support Derived from the Scenarios;62
9.7;2.6 Summary and Outlook;66
9.8;Acknowledgments;67
10;3 An Introduction to Semantic Web Technologies;70
10.1;Abstract;70
10.2;3.1 Introduction;70
10.3;3.2 The Semantic Web: Motivation, History, and Relevance for Engineering;71
10.3.1;3.2.1 Why Was the Semantic Web Needed?;71
10.3.2;3.2.2 The Semantic Web in a Nutshell;72
10.3.3;3.2.3 The Use of Semantic Web Technologies in Enterprises;74
10.3.4;3.2.4 How Are SWTs Relevant for Engineering Applications?;75
10.4;3.3 Ontologies;76
10.5;3.4 Semantic Web Languages;79
10.5.1;3.4.1 Resource Description Framework (RDF);79
10.5.2;3.4.2 RDF Schema—RDF(S);83
10.5.3;3.4.3 The Web Ontology Language (OWL);84
10.5.4;3.4.4 SPARQL (SPARQL Protocol and RDF Query Language);85
10.6;3.5 Formality and Reasoning;87
10.7;3.6 Linked Data;89
10.8;3.7 Semantic Web Capabilities Relevant for Engineering Needs;91
10.9;3.8 Summary;95
10.10;Acknowledgments;96
10.11;References;96
11;Semantic Web Enabled Data Integration in Multi-disciplinary Engineering;99
12;4 The Engineering Knowledge Base Approach;100
12.1;4.1 Introduction;100
12.2;4.2 Background and Research Challenges;102
12.2.1;4.2.1 Automation Systems Engineering;102
12.2.2;4.2.2 Semantic Integration of Tool Data Models;103
12.2.3;4.2.3 Research Challenges;104
12.3;4.3 Related Work;105
12.3.1;4.3.1 Usage of Standards in Development Processes;106
12.3.2;4.3.2 Usage of Common Project Repositories;106
12.3.3;4.3.3 Complete Transformation Between Project Data Models;107
12.4;4.4 Engineering Knowledge Base Framework;108
12.4.1;4.4.1 Engineering Knowledge Base (EKB) Overview;108
12.4.2;4.4.2 Data Structuring in the EKB Framework;109
12.5;4.5 Case Study and Evaluation;111
12.5.1;4.5.1 Case Study Description;111
12.5.2;4.5.2 Scenario-Based Evaluation of the EKB;112
12.6;4.6 Conclusion;116
12.7;References;117
13;5 Semantic Modelling and Acquisition of Engineering Knowledge;119
13.1;Abstract;119
13.2;5.1 Introduction;120
13.3;5.2 Ontology Engineering Methodologies;121
13.4;5.3 Ontology Evaluation;124
13.5;5.4 Classification of Engineering Ontologies;126
13.5.1;5.4.1 The Product-Process-Resource Abstraction;127
13.5.2;5.4.2 A Classification Scheme for Engineering Ontologies;128
13.6;5.5 Examples of Engineering Ontologies;131
13.6.1;5.5.1 The AutomationML Ontology;135
13.6.2;5.5.2 Common Concepts Ontology;138
13.7;5.6 Ontology Design Patterns for Engineering;140
13.8;5.7 Acquisition of Semantic Knowledge from Engineering Artefacts;143
13.9;5.8 Summary and Future Work;146
13.10;Acknowledgments;147
13.11;References;147
14;6 Semantic Matching of Engineering Data Structures;151
14.1;6.1 Introduction;151
14.2;6.2 Ontology Matching: Background Information and Definitions;153
14.3;6.3 Running Example: The Power Plant Engineering Project;155
14.4;6.4 Representing Relations Between Engineering Objects;157
14.5;6.5 Languages and Technologies for Mapping Definition and Representation;163
14.6;6.6 Representing Complex Relations with EDOAL;166
14.7;6.7 Conclusion;170
14.8;References;171
15;7 Knowledge Change Management and Analysis in Engineering;172
15.1;Abstract;172
15.2;7.1 Introduction;173
15.3;7.2 KCMA in Engineering;174
15.3.1;7.2.1 KCMA Example;175
15.3.2;7.2.2 Requirements for KCMA in Engineering;176
15.4;7.3 Solutions for KCMA in the Engineering Domain;177
15.4.1;7.3.1 Database Schema Evolution and Versioning;178
15.4.2;7.3.2 Model-Based Engineering (MBE) Co-Evolution;178
15.5;7.4 Semantic Web for KCMA in Engineering;179
15.5.1;7.4.1 Ontology Change Management;181
15.6;7.5 Reference Process for KCMA in MDEng Environment;185
15.7;7.6 A Potential Semantic Web-Based Implementation of the KCMA Reference Process;187
15.8;7.7 Summary and Future Work;189
15.9;Acknowledgments;189
15.10;References;189
16;Intelligent Applications for Multi-disciplinary Engineering;192
17;8 Semantic Data Integration: Tools and Architectures;193
17.1;Abstract;193
17.2;8.1 Introduction;194
17.3;8.2 Related Work;197
17.3.1;8.2.1 Semantic Web Technologies;197
17.3.2;8.2.2 Semantic Data Integration;198
17.3.3;8.2.3 Engineering Knowledge Base;199
17.3.4;8.2.4 Semantic Data Stores;200
17.3.4.1;8.2.4.1 Ontology in File Stores;201
17.3.4.2;8.2.4.2 Ontology in Triple Stores;201
17.3.4.3;8.2.4.3 Ontology in Relational Databases;202
17.3.5;8.2.5 NoSQL Graph Databases;203
17.3.6;8.2.6 Versioning;204
17.4;8.3 Use Case: A Steel Mill Plant Engineering;206
17.4.1;8.3.1 Integration Requirements;208
17.4.1.1;8.3.1.1 Data Insertion;208
17.4.1.2;8.3.1.2 Data Transformation;209
17.4.1.3;8.3.1.3 Data Query;209
17.5;8.4 Engineering Knowledge Base Software Architecture Variants;210
17.5.1;8.4.1 Software Architecture Variant A—Ontology Store;210
17.5.2;8.4.2 Software Architecture Variant B—Relational Database with RDF2RDB Mapper;211
17.5.3;8.4.3 Software Architecture Variant C—Graph Database Store;212
17.5.4;8.4.4 Software Architecture Variant D—Versioning Management System;214
17.6;8.5 Evaluation;215
17.6.1;8.5.1 Evaluation Process and Setup;216
17.6.2;8.5.2 Evaluation of Data Management Capabilities;217
17.6.2.1;8.5.2.1 Performance Results of Evaluation Scenario 1;217
17.6.2.2;8.5.2.2 Performance Results of Evaluation Scenario 2;218
17.6.3;8.5.3 Evaluation of Historical Data Analysis Capabilities;219
17.7;8.6 Discussion;222
17.8;8.7 Conclusion;225
17.9;Acknowledgments;225
17.10;References;225
18;9 Product Ramp-up for Semiconductor Manufacturing Automated Recommendation of Control System Setup;230
18.1;Abstract;230
18.2;9.1 Introduction;231
18.3;9.2 Definition of Product Ramp-up;232
18.3.1;9.2.1 In-Depth Insight into the Product Ramp-up;232
18.3.2;9.2.2 A Knowledge System Based Product Ramp-up (K-RAMP);236
18.4;9.3 Challenge of IC Production—Prerequisites for Efficient Product Ramp-up;239
18.5;9.4 The Process Perspective of K-R242
18.6;9.5 Requirements of the K-RAMP Knowledge Base;251
18.7;9.6 Architecture and Ontology Models;256
18.8;9.7 Reuse of Process Control Settings;259
18.9;9.8 Conclusions and Outlook;263
18.10;Acknowledgment;264
18.11;References;265
19;10 Ontology-Based Simulation Design and Integration;267
19.1;10.1 Motivation;268
19.2;10.2 Related Work;270
19.2.1;10.2.1 Simulation Model Design;270
19.2.2;10.2.2 Simulation Model Integration;271
19.3;10.3 Simulation Process;273
19.4;10.4 Simulation Domain Architecture;275
19.4.1;10.4.1 Simulation Framework;275
19.4.2;10.4.2 Data Sources and Data;276
19.4.3;10.4.3 Simulation Modules;278
19.5;10.5 Knowledge Base;278
19.6;10.6 Model-Driven Configurations;281
19.7;10.7 Simulation Model Design;283
19.8;10.8 Conclusions and Future Work;285
19.9;References;286
20;Related and Emerging Trends in the Use of Semantic Web in Engineering;288
21;11 Semantic Web Solutions in Engineering;289
21.1;Abstract;289
21.2;11.1 Introduction;290
21.3;11.2 Semantic Web Solutions for Model Integration;292
21.4;11.3 Semantic Web Solutions for Model Consistency Management;294
21.5;11.4 Semantic Web Solutions for Flexible Comparison;297
21.6;11.5 Conclusions;298
21.7;11.6 Outlook on Part IV;300
21.8;Acknowledgments;303
21.9;References;303
22;12 Semantic Web Solutions in the Automotive Industry;305
22.1;12.1 Introduction: Models in the Engineering Domain;306
22.2;12.2 Systems Engineering and SysML;307
22.3;12.3 The Engineering Ontologies;308
22.3.1;12.3.1 Representing the Engineering Ontologies;309
22.3.2;12.3.2 Why Frames and Not OWL;310
22.3.3;12.3.3 The Components Ontology;312
22.3.4;12.3.4 The Connections Ontology;313
22.3.5;12.3.5 The Systems Ontology;314
22.3.6;12.3.6 The Requirements Ontology;316
22.3.7;12.3.7 The Constraints Ontology;317
22.4;12.4 Use Case 1: Stepwise Refinement of Design Requirements;318
22.4.1;12.4.1 The Requirements Management System;319
22.4.2;12.4.2 The User Interface;320
22.4.3;12.4.3 The Requirements Ontology in SDD;321
22.4.4;12.4.4 The Constraint Processing Logic;322
22.4.5;12.4.5 The Automatic Conflict Solving;323
22.4.6;12.4.6 The SDD Application at Runtime;323
22.4.7;12.4.7 Benefits of an Ontology--Based Approach;324
22.5;12.5 Use Case 2: Mapping and Change Propagation between Engineering Models;324
22.5.1;12.5.1 Mapping Between Libraries of Components;325
22.5.2;12.5.2 The Mapping Framework;326
22.5.3;12.5.3 Defining the Mappings;329
22.5.4;12.5.4 Consistency Checking and Change Propagation;329
22.5.5;12.5.5 Benefits of an Ontology--Based Approach;330
22.6;12.6 Conclusion;331
22.7;References;332
23;13 Leveraging Semantic Web Technologies for Consistency Management in Multi-viewpoint Systems Engineering;335
23.1;13.1 Introduction;336
23.2;13.2 Utilizing Semantic Web Technologies for Validating Integrated System Components;339
23.2.1;13.2.1 Reasoning over Ontologies;340
23.2.2;13.2.2 Validation of RDF Data;341
23.3;13.3 Shapes Constraint Language (SHACL);343
23.3.1;13.3.1 Preliminaries;344
23.3.2;13.3.2 Identifying Nodes for Validation;345
23.3.3;13.3.3 SHACL Constraint Types;346
23.3.4;13.3.4 SHACL Constraint Components;347
23.3.5;13.3.5 Reporting of Validation Results;348
23.4;13.4 Use Case: Integrating Heterogeneous Views on a Computer Network;348
23.4.1;13.4.1 Integration of Heterogeneous Viewpoints;349
23.4.2;13.4.2 Defining Mappings Between Viewpoint Definitions using SHACL;349
23.5;13.5 Related Work;354
23.6;13.6 Conclusion;356
23.7;References;357
24;14 Applications of Semantic Web Technologies for the Engineering of Automated Production Systems---Three Use Cases;361
24.1;14.1 Introduction;362
24.2;14.2 Application Example: The Pick and Place Unit;363
24.3;14.3 Challenges in the Automated Production Systems Domain;365
24.4;14.4 Related Works in the Field of Inconsistency Management;366
24.5;14.5 Semantic Web Technologies in a Nutshell;367
24.6;14.6 Use Cases for Applying Semantic Web Technologies in the Automated Production Systems Domain;371
24.6.1;14.6.1 Use Case 1: Ensuring the Compatibility Between Mechatronic Modules;371
24.6.2;14.6.2 Use Case 2: Keeping Requirements and Test Cases Consistent;376
24.6.3;14.6.3 Use Case 3: Identifying Inconsistencies in and Among Heterogeneous Engineering Models;381
24.7;14.7 Conclusion and Directions for Future Research;387
24.8;References;388
25;15 Conclusions and Outlook;391
25.1;Abstract;391
25.2;15.1 Introduction;391
25.3;15.2 Semantic Web Technologies for Building Intelligent Engineering Applications: Capabilities and Limitations;392
25.3.1;15.2.1 Industrie 4.0 Scenarios and Tasks Solved with SWTs;392
25.3.2;15.2.2 Most Used Semantic Web Capabilities;395
25.3.3;15.2.3 Least Used Semantic Web Capabilities;398
25.3.4;15.2.4 Semantic Web Limitations and Challenges;398
25.3.5;15.2.5 Alternative Technologies;400
25.4;15.3 A Technology Blueprint for IEAa;402
25.5;15.4 Outlook;404
25.6;Acknowledgments;406
25.7;References;407
26;Index;409