情境中的模糊计算本体

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

计算本体（computational ontology）是对概念以及概念间的各种关系的一种形式化表述，是知识表示、语义网、智能主体等人工智能主要研究领域中的重要研究对象。《情境中的模糊计算本体（英文版）》提出了一个基于模糊集的、可表达对象对于概念的归属程度（object membership）和对象在概念中的典型程度（object typicality）的形式化计算本体模型，以具体例子论证了此形式化模型的必要性和重要性；指出了情境（context）对物体归属程度和典型程度的影响，并对此加以形式化；最后讨论了此形式化模型在推荐系统中的应用，用实验证明利用对象典型程度，或把对象典型程度加到协同过滤法后，能进一步提高模型的准确性。