情境中的模糊计算本体（ 英文版）

Chapter 1 Introduction
1.1 Semantic Web and Ontologies
1.2 Motivations
1.2.1 Fuzziness of Concepts
1.2.2 Typicality of Objects in Concepts
1.2.3 Context and Its Efiect on Reasoning
1.3 Our Work
1.3.1 Objectives
1.3.2 Contributions
1.4 Structure of the Book
References
Chapter 2 Knowledge Representation on the Wleb
2.1 Semantic Web
2.2 Ontologies
2.3 Description Logics
References
Chapter 3 Concepts and Categorization from a Psychological Perspective
3.1 Theory of Concepts
3.1.1 Classical View
3.1.2 Prototype View
3.1.3 Other Views
3.2 Membership versus Typicality
3.3 Similarity Between Concepts
3.4 Context and Context Efiects
References
Chapter 4 Modeling Uncertainty in Knowledge
Representation
4.1 Fuzzy Set Theory
4.2 Uncertainty in Ontologies and Description Logics
4.3 Semantic Similarity
4.4 Contextual Reasoning
4.5 Summary
References
Chapter 5 Fuzzy Ontology：A First Formal Model
5.1 Rationale
5.2 Concepts and Properties
5.3 Subsumption of Concepts
5.4 Object Membership of an Individual in a Concept
5.5 Prototype Vector and Typicality
5.6 An Example
5.7 Properties of the Proposed Model
5.7.1 Object Membership
5.7.2 Typicality
5.8 On Object Membership and Typicality
5.9 Summary
References
Chapter 6 A More General Ontology Model with ObjectMembership and Typicality
6.1 Motivation
6.2 Limitations of Previous Models
6.2.1 Limitation of Previous Modds in Measuring Object Membership
6.2.2 Limitations of Previous Models in Measuring Object Typicality
6.3 A Better Conceptual Model of Fuzzy Ontology
6.3.1 A Novel Fuzzy Ontology Model
6.3.2 Two Kinds of Measurements of Objects Possessing Properties
6.3.3 Concepts Represented by N-Properties and L-Properties
6.4 Fuzzy Membership of Objects in Concepts
6.4.1 Measuring Degrees of Objects Possessing Defining Properties of Concepts
……
Chapter 7 Context-aware Object Typicality Measurement in Fuzzy Ontology
Chapter 8 Object Membership with Property Importance and Property Priority
Chapter 9 Applications
Chapter 10 Conclusions and Future Work
Index