| Description |
1 online resource |
| Series |
Advances in intelligent systems and computing, 2194-5357 ; v. 626 |
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Advances in intelligent systems and computing ; 626.
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| Contents |
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 |
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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 |
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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 |
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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 |
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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 |
| Summary |
This book compiles a number of contributions originating from the KESE (Knowledge Engineering and Software Engineering) workshop series from 2005 to 2015. The idea behind the series was the realignment of the knowledge engineering discipline and its strong relation to software engineering, as well as to the classical aspects of artificial intelligence research. The book introduces symbiotic work combining these disciplines, such as aspect-oriented and agile engineering, using anti-patterns, and system refinement. Furthermore, it presents successful applications from different areas that were created by combining techniques from both areas |
| Bibliography |
Includes bibliographical references |
| Notes |
Print version record |
| Subject |
Software engineering.
|
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COMPUTERS -- Software Development & Engineering -- General.
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Software engineering
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| Form |
Electronic book
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| Author |
Nalepa, Grzegorz J
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|
Baumeister, Joachim
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| ISBN |
9783319641614 |
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3319641611 |
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