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Table of Contents
Foreword I; Foreword II; Preface; Contents; Contributors; Abbreviations; 1 Introduction; Abstract; 1.1 Context and Aims of This Book; 1.2 Industrial Production Systems; 1.3 Intelligent Engineering Applications for Industrie 4.0; 1.4 Who Should Read This Book and Why?; 1.5 Book Content and Structure; Acknowledgments; References; Background and Requirements of Industrie 4.0 for Semantic Web Solutions; 2 Multi-Disciplinary Engineering for Industrie 4.0: Semantic Challenges and Needs; Abstract; 2.1 Introduction; 2.2 Production Systems Life Cycle; 2.3 Engineering of Industrial Production Systems.
2.4 Usage Scenarios that Illustrate Needs for Semantic Support2.4.1 Scenario 1-Discipline-Crossing Engineering Tool Networks; 2.4.2 Scenario 2-Use of Existing Artifacts for Plant Engineering; 2.4.3 Scenario 3-Flexible Production System Organization; 2.4.4 Scenario 4-Maintenance and Replacement Engineering; 2.5 Needs for Semantic Support Derived from the Scenarios; 2.6 Summary and Outlook; Acknowledgments; 3 An Introduction to Semantic Web Technologies; Abstract; 3.1 Introduction; 3.2 The Semantic Web: Motivation, History, and Relevance for Engineering; 3.2.1 Why Was the Semantic Web Needed?
3.2.2 The Semantic Web in a Nutshell3.2.3 The Use of Semantic Web Technologies in Enterprises; 3.2.4 How Are SWTs Relevant for Engineering Applications?; 3.3 Ontologies; 3.4 Semantic Web Languages; 3.4.1 Resource Description Framework (RDF); 3.4.2 RDF Schema-RDF(S); 3.4.3 The Web Ontology Language (OWL); 3.4.4 SPARQL (SPARQL Protocol and RDF Query Language); 3.5 Formality and Reasoning; 3.6 Linked Data; 3.7 Semantic Web Capabilities Relevant for Engineering Needs; 3.8 Summary; Acknowledgments; References; Semantic Web Enabled Data Integration in Multi-disciplinary Engineering.
4 The Engineering Knowledge Base Approach4.1 Introduction; 4.2 Background and Research Challenges; 4.2.1 Automation Systems Engineering; 4.2.2 Semantic Integration of Tool Data Models; 4.2.3 Research Challenges; 4.3 Related Work; 4.3.1 Usage of Standards in Development Processes; 4.3.2 Usage of Common Project Repositories; 4.3.3 Complete Transformation Between Project Data Models; 4.4 Engineering Knowledge Base Framework; 4.4.1 Engineering Knowledge Base (EKB) Overview; 4.4.2 Data Structuring in the EKB Framework; 4.5 Case Study and Evaluation; 4.5.1 Case Study Description.
4.5.2 Scenario-Based Evaluation of the EKB4.6 Conclusion; References; 5 Semantic Modelling and Acquisition of Engineering Knowledge; Abstract; 5.1 Introduction; 5.2 Ontology Engineering Methodologies; 5.3 Ontology Evaluation; 5.4 Classification of Engineering Ontologies; 5.4.1 The Product-Process-Resource Abstraction; 5.4.2 A Classification Scheme for Engineering Ontologies; 5.5 Examples of Engineering Ontologies; 5.5.1 The AutomationML Ontology; 5.5.2 Common Concepts Ontology; 5.6 Ontology Design Patterns for Engineering; 5.7 Acquisition of Semantic Knowledge from Engineering Artefacts.
2.4 Usage Scenarios that Illustrate Needs for Semantic Support2.4.1 Scenario 1-Discipline-Crossing Engineering Tool Networks; 2.4.2 Scenario 2-Use of Existing Artifacts for Plant Engineering; 2.4.3 Scenario 3-Flexible Production System Organization; 2.4.4 Scenario 4-Maintenance and Replacement Engineering; 2.5 Needs for Semantic Support Derived from the Scenarios; 2.6 Summary and Outlook; Acknowledgments; 3 An Introduction to Semantic Web Technologies; Abstract; 3.1 Introduction; 3.2 The Semantic Web: Motivation, History, and Relevance for Engineering; 3.2.1 Why Was the Semantic Web Needed?
3.2.2 The Semantic Web in a Nutshell3.2.3 The Use of Semantic Web Technologies in Enterprises; 3.2.4 How Are SWTs Relevant for Engineering Applications?; 3.3 Ontologies; 3.4 Semantic Web Languages; 3.4.1 Resource Description Framework (RDF); 3.4.2 RDF Schema-RDF(S); 3.4.3 The Web Ontology Language (OWL); 3.4.4 SPARQL (SPARQL Protocol and RDF Query Language); 3.5 Formality and Reasoning; 3.6 Linked Data; 3.7 Semantic Web Capabilities Relevant for Engineering Needs; 3.8 Summary; Acknowledgments; References; Semantic Web Enabled Data Integration in Multi-disciplinary Engineering.
4 The Engineering Knowledge Base Approach4.1 Introduction; 4.2 Background and Research Challenges; 4.2.1 Automation Systems Engineering; 4.2.2 Semantic Integration of Tool Data Models; 4.2.3 Research Challenges; 4.3 Related Work; 4.3.1 Usage of Standards in Development Processes; 4.3.2 Usage of Common Project Repositories; 4.3.3 Complete Transformation Between Project Data Models; 4.4 Engineering Knowledge Base Framework; 4.4.1 Engineering Knowledge Base (EKB) Overview; 4.4.2 Data Structuring in the EKB Framework; 4.5 Case Study and Evaluation; 4.5.1 Case Study Description.
4.5.2 Scenario-Based Evaluation of the EKB4.6 Conclusion; References; 5 Semantic Modelling and Acquisition of Engineering Knowledge; Abstract; 5.1 Introduction; 5.2 Ontology Engineering Methodologies; 5.3 Ontology Evaluation; 5.4 Classification of Engineering Ontologies; 5.4.1 The Product-Process-Resource Abstraction; 5.4.2 A Classification Scheme for Engineering Ontologies; 5.5 Examples of Engineering Ontologies; 5.5.1 The AutomationML Ontology; 5.5.2 Common Concepts Ontology; 5.6 Ontology Design Patterns for Engineering; 5.7 Acquisition of Semantic Knowledge from Engineering Artefacts.