Jiang Yuncheng, Wang Ju, Deng Peimin, Tang Suqin
School of Computer Science and Information Technology, Guangxi Normal University, Guilin 541004, PR China.
Fuzzy Sets Syst. 2009 Dec 1;160(23):3403-3424. doi: 10.1016/j.fss.2009.01.004. Epub 2009 Jan 26.
It is generally accepted that the management of imprecision and vagueness will yield more intelligent and realistic knowledge-based applications. Description Logics (DLs) are suitable, well-known logics for managing structured knowledge that have gained considerable attention the last decade. The current research progress and the existing problems of uncertain or imprecise knowledge representation and reasoning in DLs are analyzed in this paper. An integration between the theories of fuzzy DLs and rough DLs has been attempted by providing fuzzy rough DLs based on fuzzy rough set theory. The syntax, semantics and properties of fuzzy rough DLs are given. It is proved that the satisfiability, subsumption, entailment and ABox consistency reasoning in fuzzy rough DLs may be reduced to the ABox consistency reasoning in the corresponding fuzzy DLs.
人们普遍认为，对不精确性和模糊性的管理将产生更智能、更现实的基于知识的应用程序。描述逻辑（DLs）是适用于管理结构化知识的著名逻辑，在过去十年中受到了广泛关注。本文分析了描述逻辑中不确定或不精确知识表示与推理的研究现状及存在的问题。通过基于模糊粗糙集理论提供模糊粗糙描述逻辑，尝试将模糊描述逻辑和粗糙描述逻辑的理论进行整合。给出了模糊粗糙描述逻辑的语法、语义和性质。证明了模糊粗糙描述逻辑中的可满足性、包含关系、蕴含关系和ABox一致性推理可归结为相应模糊描述逻辑中的ABox一致性推理。
