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Fuzzy Computational Ontologies in Contexts （情境中的模糊计算本体, 英文版）

样章
作者：
Yi Cai, et al.
定价：
49.00 元
版面字数：
350千字
开本：
16开
装帧形式：
精装
版次：
1
最新版次
印刷时间：
2011年
ISBN：
978-7-04-033889-8
物料号：
33889-00
出版时间：
2011-12-09
读者对象：
学术著作
一级分类：
自然科学
二级分类：
计算机科学与工程
三级分类：
计算理论与算法

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。

  • 目录
    • 前辅文
      • 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 E®ect 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 Web
                        • 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 E®ects
                                            • 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 Object Membership and Typicality
                                                                                • 6.1 Motivation
                                                                                  • 6.2 Limitations of Previous Models
                                                                                    • 6.2.1 Limitation of Previous Models 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 De¯ning Properties of Concepts
                                                                                                • 6.4.2 Calculation of Object Fuzzy Memberships in Concepts
                                                                                                  • 6.4.3 Discussion
                                                                                                  • 6.5 Object Typicality in Concepts
                                                                                                    • 6.5.1 Representation of Concepts and Objects based on Prototype View
                                                                                                      • 6.5.2 Similarity and Dissimilarity Measurement Between Objects and Prototypes
                                                                                                        • 6.5.3 Modeling In°uencing Factors of Typicality
                                                                                                          • 6.5.4 Discussion
                                                                                                          • 6.6 Summary
                                                                                                            • References
                                                                                                            • Chapter 7 Context-aware Object Typicality Measurement in Fuzzy Ontology
                                                                                                              • 7.1 Motivation
                                                                                                                • 7.2 Modeling Context in Ontology
                                                                                                                  • 7.3 Measuring Object Typicality in Context-aware Ontology
                                                                                                                    • 7.3.1 Modeling In°uencing Factors of Typicality
                                                                                                                      • 7.3.2 Context E®ects on In°uencing Factors of Object Typicality
                                                                                                                        • 7.3.3 Measuring Typicality
                                                                                                                        • 7.4 Empirical Evaluation
                                                                                                                          • 7.5 Discussion
                                                                                                                            • 7.5.1 Context E®ects on Measuring Object Typicality in Our Model
                                                                                                                              • 7.5.2 Di®erences Between Various Vectors in Our Model
                                                                                                                              • 7.6 Summary
                                                                                                                                • References
                                                                                                                                • Chapter 8 Object Membership with Property Importance and Property Priority
                                                                                                                                  • 8.1 Motivation
                                                                                                                                    • 8.2 A Formal Model of Fuzzy Ontology with Property Importance and Property Priority
                                                                                                                                      • 8.2.1 A Conceptual Model of Fuzzy Ontology
                                                                                                                                        • 8.2.2 Modeling Property Importance
                                                                                                                                          • 8.2.3 Modeling Property Priority
                                                                                                                                          • 8.3 Measuring Object Membership in Concepts with Property Importance and Priority
                                                                                                                                            • 8.3.1 Local Satisfaction Degrees of Objects for Properties
                                                                                                                                              • 8.3.2 Global Satisfaction Degrees of Objects for Characteristic Vectors with Weighted Properties
                                                                                                                                                • 8.3.3 Global Satisfaction Degrees of Objects for Characteristic Vectors with Prioritized Properties
                                                                                                                                                  • 8.3.4 Measuring Object Membership by Aggregating Global Satisfaction Degrees
                                                                                                                                                  • 8.4 Discussions
                                                                                                                                                    • 8.4.1 Di®erences Between Property Importance and Property Priority
                                                                                                                                                      • 8.4.2 Illustrating Examples
                                                                                                                                                      • 8.5 Experiment
                                                                                                                                                        • 8.5.1 Evaluation on Concepts with Property Importance
                                                                                                                                                          • 8.5.2 Evaluation on Concepts with Property Priority
                                                                                                                                                          • 8.6 Summary
                                                                                                                                                            • References
                                                                                                                                                            • Chapter 9 Applications
                                                                                                                                                              • 9.1 Overview
                                                                                                                                                                • 9.1.1 Motivation
                                                                                                                                                                  • 9.1.2 ROT
                                                                                                                                                                    • 9.1.3 TyCo
                                                                                                                                                                    • 9.2 Related Work of Recommender Systems
                                                                                                                                                                      • 9.2.1 Content-based Recommender Systems
                                                                                                                                                                        • 9.2.2 Collaborative Filtering Recommender Systems
                                                                                                                                                                          • 9.2.3 Characteristics of Collaborative Filtering
                                                                                                                                                                            • 9.2.4 Model-based and Memory-based Methods
                                                                                                                                                                              • 9.2.5 Hybrid Recommender Systems
                                                                                                                                                                              • 9.3 ROT: Typicality-based Recommendation
                                                                                                                                                                                • 9.3.1 A Recommendation Method based on Typicality
                                                                                                                                                                                  • 9.3.2 Measuring Typicality Degrees of Items in Item Groups
                                                                                                                                                                                    • 9.3.3 Measuring Typicality Degrees of Users in User Groups
                                                                                                                                                                                      • 9.3.4 Conversion Function
                                                                                                                                                                                      • 9.4 TyCo: Typicality-based Collaborative Filtering
                                                                                                                                                                                        • 9.4.1 Overview of TyCo
                                                                                                                                                                                          • 9.4.2 Mechanism of TyCo
                                                                                                                                                                                            • 9.4.3 Neighbor Selection
                                                                                                                                                                                              • 9.4.4 Prediction
                                                                                                                                                                                              • 9.5 Evaluation
                                                                                                                                                                                                • 9.5.1 Data Set Description
                                                                                                                                                                                                  • 9.5.2 Metrics
                                                                                                                                                                                                    • 9.5.3 Experiment Process
                                                                                                                                                                                                      • 9.5.4 Experiment Results
                                                                                                                                                                                                      • 9.6 Discussion
                                                                                                                                                                                                        • 9.6.1 Di®erence Between Previous Recommendation Methods and ROT
                                                                                                                                                                                                          • 9.6.2 Di®erence Between Cluster-based Collaborative Filtering Methods and TyCo
                                                                                                                                                                                                            • 9.6.3 Other In°uencing Factors
                                                                                                                                                                                                            • 9.7 Summary
                                                                                                                                                                                                              • References
                                                                                                                                                                                                              • Chapter 10 Conclusions and Future Work
                                                                                                                                                                                                                • 10.1 Conclusions
                                                                                                                                                                                                                  • 10.2 Future Research Directions
                                                                                                                                                                                                                    • References
                                                                                                                                                                                                                    • Index

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