情境中的模糊計算本體

內(nèi)容概要

計算本體(computational
ontology)是對概念以及概念間的各種關(guān)系的一種形式化表述，是知識表示、語義網(wǎng)、智能主體等人工智能主要研究領(lǐng)域中的重要研究對象。本書提出了一個基于模糊集的、可表達(dá)對象對于概念的歸屬程度(object
membership)和對象在概念中的典型程度(object
typicality)的形式化計算本體模型，以具體例子論證了此形式化模型的必要性和重要性；指出了情境(context)對物體歸屬程度和典型程度的影響，并對此加以形式化；最后討論了此形式化模型在推薦系統(tǒng)中的應(yīng)用，用實驗證明利用對象典型程度，或把對象典型程度加到協(xié)同過濾法后，能進(jìn)一步提高模型的準(zhǔn)確性。

書籍目錄

Chapter 1 Introduction
Chapter 2 Knowledge Representation on the Web
Chapter 3 Concepts and Categorization from a Psychological
Perspective
Chapter 4 Modeling Uncertainty in Knowledge
Representation
Chapter 5 Fuzzy Ontology: A First Formal Model
Chapter 6 A More General Ontology Model with Object
Membership and Typicality
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

章節(jié)摘錄

版權(quán)頁：插圖：From this description，we notice that besides what we call vagueness in con-cepts，we also have another issue of whether an individual object iS typicalor not.For example，when we refer to concept‘bird’，we may reminder spar.rows and eagles，which are typical instances of the concept‘bird’.and rarelyreminder penguins and ostrich，which are not typical instances of‘bird’.Atfirst glance.such‘typicality’of individual objects in concepts can be treatedin the same way as in the case of vagueness.and in fact they can be bothmodeled by fuzzy set theory or probabilistic theory in some previous works（e.g.，[24，25]）.Most of the existing approaches only focus on the fuzzinessor vagueness of concepts but not on this typicality effect of categorizations.In fact.fuzziness and typicality are actually intrinsically different aspects ofconcepts.As mentioned in Ref.『26l，we can identify two types of measuresof an individual object’S membership in a concept.referring to fuzziness andtypicality.That different individual objects have different degrees of typical-ity（or prototypicality）in a Certain concept iS actually first studied in thefield of cognitive psychology[27-29].As works in cognitive psychology sug-gest，typicality iS more a psychological effect than an objective decision of anindividual’S membership grade in a concept.It iS found out that typicality ofobjects depends on the match of necessary properties as well as non.necessaryproperties28.For example，robins are generally considered as more typicalbirds than penguins I 28 I.This iS probably due to the fact that birds are gen-erally considered to be able to fly,but penguins do not.Hence，we can seethat this iS very different from，say，how we.judge a certain temperature as‘high’or not.Thus，typicality should be determined by a different mechanismfrom the one used to determine the fuzzy membership grade of an individualobject.While it iS desirable to model fuzziness of concepts in ontologies.theeffect of typicality should not be overlooked.We believe that it is necessaryto identify the differences between the two measures，SO that we are able tocome up with formal methods to model these two measures in ontologies.

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《情境中的模糊計算本體(英文版)》是國家科學(xué)技術(shù)學(xué)術(shù)著作出版基金資助的新一代信息科學(xué)與技術(shù)。

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