图书信息
图书目录
Fuzzy Computational Ontologies in Contexts （情境中的模糊计算本体, 英文版）
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。
作者：
Yi Cai, et al.
定价：
49.00元
出版时间：
2011-12-09
ISBN：
978-7-04-033889-8
物料号：
33889-00
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
重点项目：
暂无
版面字数：
350.000千字
开本：
16开
全书页数：
202页
装帧形式：
精装
- 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
- 3.1 Theory of Concepts
- 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
- 9.1 Overview
- Chapter 10 Conclusions and Future Work
- 10.1 Conclusions
- 10.2 Future Research Directions
- References
- Index
