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Extending description logics with uncertainty reasoning in possibilistic logic
Author(s) -
Qi Guilin,
Ji Qiu,
Pan Jeff Z.,
Du Jianfeng
Publication year - 2011
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20470
Subject(s) - description logic , inference , ontology , axiom , semantics (computer science) , t norm fuzzy logics , computer science , rule of inference , knowledge base , artificial intelligence , theoretical computer science , mathematics , fuzzy logic , fuzzy set , programming language , fuzzy number , epistemology , philosophy , geometry
Possibilistic logic provides a convenient tool for dealing with uncertainty and handling inconsistency. In this paper, we propose possibilistic description logics as an extension of description logics, which are a family of well‐known ontology languages. We first give the syntax and semantics of possibilistic description logics and define several inference services in possibilistic description logics. We show that these inference serviced can be reduced to the task of computing the inconsistency degree of a knowledge base in possibilistic description logics. Since possibilistic inference services suffer from the drowning problem , that is, axioms whose confidence degrees are less than or equal to the inconsistency are not used, we consider a drowning‐free variant of possibilistic inference, called linear order inference. We propose an algorithm for computing the inconsistency degree of a possibilistic description logic knowledge base and an algorithm for the linear order inference. We consider the impact of our possibilistic description logics on ontology learning and ontology merging. Finally, we implement these algorithms and provide some interesting evaluation results. © 2011 Wiley Periodicals, Inc.