By Dr. Yi Cai, Dr. Ching-man Au Yeung, Prof. Ho-fung Leung (auth.)
Knowledge illustration performs a necessary function in Semantic net, particularly in computerized details processing and communications between software program brokers. This ebook, entitled "Fuzzy Computational Ontologies in Contexts: Formal types of information illustration with club measure and Typicality, and Their Applications", discusses wisdom illustration in Semantic internet. It introduces the suitable historical past wisdom, versions of fuzzy ontologies, significance and precedence of houses in recommendations, and item typicality in fuzzy ontologies and context-aware ontologies.
The e-book is meant for graduate scholars, engineers, and researchers within the box of synthetic intelligence and laptop science.
Yi Cai is an Assistant Professor at school of software program Engineering, South China collage of expertise, Guangzhou, China. Ching-man Au Yeung is a Senior Engineer in Hong Kong utilized technology and know-how learn Institute, Hong Kong, China. Ho-fung Leung is a Professor and the Chairman of division of computing device technological know-how and Engineering, The chinese language college of Hong Kong, Hong Kong, China.
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Additional info for Fuzzy Computational Ontologies in Contexts: Formal Models of Knowledge Representation with Membership Degree and Typicality of Objects, and Their Applications
Example text
Recently, we also propose a fuzzy description logic named fom -DL for Semantic Web environment [26]. In fom -DL , there is an alphabet of distinct concepts (C), roles (R), objects (I) and properties (P). It adopts the unique name assumption in fom -DL . The syntax of fom -DL is as follows. Role. Each role name RN is a fuzzy role in fom -DL . A valid role R is defined by the abstract syntax: R := RN |R− . The inverse relation of roles is symmetric, and to avoid considering roles such as R−− , we defined a function Inv, which returns the inverse of a role, more precisely Inv(R) := RN − if R = RN , and Inv(R) = RN if R = RN − .
For example, its formulation of graded membership helps to explain why some objects are considered better examples of a given category than other objects. It also explains why sometimes it is difficult for people to come up with a clear definition of some concepts we encounter in our daily live. 3 Other Views Although the prototype view has the ability to account for many different aspects of how concepts and properties are represented in the human mind, there are also other situations in which it fails to give a thorough explanation [4, 13].
The thorough review paper by Komatsu [19], along with the cited references, can be referred to for more detailed explanation of the different views mentioned above. We note that seeking for an explanation of the typicality effect is a problem central to all different views proposed in response to the limitation of the classical view, and that views such as the exemplar view and the schema view are actually established based on the ideas of the prototype view. Hence, in this book, we focus on investigating how existing ontology models can be extended to handle typicality in categorization, by making use of the ideas in the prototype view.