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Preface; Contents; Contributors; Part I Methodological Studies; Aspect-Oriented Ontology Development; 1 Introduction; 2 Aspect-Oriented Programming; 2.1 Quantification and Obliviousness; 3 Aspect-Oriented Ontologies; 3.1 Syntax; 3.2 Semantics; 4 Application Scenarios; 4.1 Research Scenarios; 4.2 Industrial Scenarios; 5 Tools; 5.1 An Annotation-Based API for Programmatic Access to Aspect-Oriented Ontologies; 5.2 Aspect OntoMaven; 5.3 Editors and Aspect Management Tools; 5.4 An Aspect Weaver for Ontologies Using Structural Ontology Design Patterns; 6 Evaluation; 6.1 Experiment; 6.2 Results
7 Conclusion and OutlookReferences; Similarity-Based Retrieval and Automatic Adaptation of Semantic Workflows; 1 Introduction; 2 Process-Oriented Case-Based Reasoning; 3 Semantic Workflows; 3.1 Workflows; 3.2 Introduction to Semantic Workflows; 3.3 Representation of Semantic Workflows; 3.4 Repository of Semantic Workflows; 4 Similarity-Based Retrieval of Workflows; 4.1 User Queries; 4.2 Semantic Workflow Similarity; 4.3 Efficient Similarity Computation by Heuristic Search; 4.4 Parallelized Similarity Computation; 4.5 Two-Step Retrieval; 4.6 Cluster-Based Retrieval
5 Workflow Adaptation and Learning Adaptation Knowledge5.1 Adaptation by Generalization and Specialization; 5.2 Compositional Adaptation; 5.3 Transformational Adaptation; 6 CAKE
An Integrated System for Process-Oriented Case-Based Reasoning; 6.1 Achitecture; 6.2 Selected Application Examples; 6.3 Required Knowledge Engineering; 7 Conclusions; References; Development of Knowledge-Based Systems Which Use Bayesian Networks; 1 Introduction; 2 Knowledge Engineering: Current State; 3 Software Engineering Methods for KBS: Model Driven Development; 4 Bayesian Networks Basics
4.1 Steps to Build a Bayesian Network4.2 Reasoning with Bayesian Networks; 5 Embedding BNs in MDE Chain; 5.1 Bayesian Network Metamodel; 5.2 UBN Profile; 5.3 BNs KBS Process Model Using UBN; 6 Case Study: A Pest Control BN-Based KBS; 7 Conclusions; References; Knowledge Acquisition During Software Development: Modeling with Anti-patterns; 1 Introduction; 2 The Knowledge Acquisition Framework; 2.1 Collecting the Relevant Data; 2.2 Forming the Anti-pattern; 2.3 Modeling the Anti-patterns with Bayesian Networks; 3 The Case Study; 3.1 CASE STUDY: The BENEFIT Platform; 3.2 Data Collection
3.3 The "Tech- Aware Manager'' Anti-pattern3.4 Knowledge-Based Models of the "Tech-Aware'' Anti-pattern; 4 Conclusions; References; Knowledge Engineering of System Refinement What We Learnt from Software Engineering; 1 Introduction; 2 Refinement Approaches; 3 A Framework for Validation and Refinement; 4 The Developed Refinement Strategy; 4.1 Finding "Guilty Rules''; 4.2 Simple Refinement by Conclusion Replacement; 4.3 Replacing the If-Part of the Remaining Guilty Rules; 4.4 Recompiling the New Rules and Removing the Unused Rules; 5 The Technique in the Context of the Assumptions
7 Conclusion and OutlookReferences; Similarity-Based Retrieval and Automatic Adaptation of Semantic Workflows; 1 Introduction; 2 Process-Oriented Case-Based Reasoning; 3 Semantic Workflows; 3.1 Workflows; 3.2 Introduction to Semantic Workflows; 3.3 Representation of Semantic Workflows; 3.4 Repository of Semantic Workflows; 4 Similarity-Based Retrieval of Workflows; 4.1 User Queries; 4.2 Semantic Workflow Similarity; 4.3 Efficient Similarity Computation by Heuristic Search; 4.4 Parallelized Similarity Computation; 4.5 Two-Step Retrieval; 4.6 Cluster-Based Retrieval
5 Workflow Adaptation and Learning Adaptation Knowledge5.1 Adaptation by Generalization and Specialization; 5.2 Compositional Adaptation; 5.3 Transformational Adaptation; 6 CAKE
An Integrated System for Process-Oriented Case-Based Reasoning; 6.1 Achitecture; 6.2 Selected Application Examples; 6.3 Required Knowledge Engineering; 7 Conclusions; References; Development of Knowledge-Based Systems Which Use Bayesian Networks; 1 Introduction; 2 Knowledge Engineering: Current State; 3 Software Engineering Methods for KBS: Model Driven Development; 4 Bayesian Networks Basics
4.1 Steps to Build a Bayesian Network4.2 Reasoning with Bayesian Networks; 5 Embedding BNs in MDE Chain; 5.1 Bayesian Network Metamodel; 5.2 UBN Profile; 5.3 BNs KBS Process Model Using UBN; 6 Case Study: A Pest Control BN-Based KBS; 7 Conclusions; References; Knowledge Acquisition During Software Development: Modeling with Anti-patterns; 1 Introduction; 2 The Knowledge Acquisition Framework; 2.1 Collecting the Relevant Data; 2.2 Forming the Anti-pattern; 2.3 Modeling the Anti-patterns with Bayesian Networks; 3 The Case Study; 3.1 CASE STUDY: The BENEFIT Platform; 3.2 Data Collection
3.3 The "Tech- Aware Manager'' Anti-pattern3.4 Knowledge-Based Models of the "Tech-Aware'' Anti-pattern; 4 Conclusions; References; Knowledge Engineering of System Refinement What We Learnt from Software Engineering; 1 Introduction; 2 Refinement Approaches; 3 A Framework for Validation and Refinement; 4 The Developed Refinement Strategy; 4.1 Finding "Guilty Rules''; 4.2 Simple Refinement by Conclusion Replacement; 4.3 Replacing the If-Part of the Remaining Guilty Rules; 4.4 Recompiling the New Rules and Removing the Unused Rules; 5 The Technique in the Context of the Assumptions