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Modeling Uncertainty in Ontologies using Rough Set

机译:使用粗糙集建模本体中的不确定性

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Modeling the uncertain aspect of the world in ontologies is attracting a lot of interests to ontologies builders especially in the World Wide Web community. This paper defines a way of handling uncertainty in description logic ontologies without remodeling existing ontologies or altering the syntax of existing ontologies modeling languages. We show that the source of vagueness in an ontology is from vague attributes and vague roles. Therefore, to have a clear separation between crisp concepts and vague concepts, the set of roles R is split into two distinct sets Rc and Rv representing the set of crisp roles and the set of vague roles respectively. Similarly, the set of attributes A was split into two distinct sets Ac and Av representing the set of crisp attributes and the set of vague attributes respectively. Concepts are therefore clearly classified as crisp concepts or vague concepts depending on whether vague attributes or vague roles are used in its conceptualization or not. The concept of rough set introduced by Pawlak is used to measure the degree of satisfiability of vague concepts as well as vague roles. In this approach, the cost of reengineering existing ontologies in order to cope with reasoning over the uncertain aspects of the world is minimal.
机译:在本体中对世界的不确定方面进行建模正在引起本体构建者的极大兴趣,尤其是在万维网社区中。本文定义了一种处理描述逻辑本体中的不确定性而无需重新构建现有本体或更改现有本体建模语言的语法的方法。我们证明了本体中模糊性的根源来自模糊的属性和模糊的角色。因此,为了清楚地区分清晰概念和模糊概念,将角色集合R分为两个不同的集合R c 和R v 和模糊角色集。类似地,将属性集A分为两个不同的集A c 和A v ,分别代表清晰属性集和模糊属性集。因此,根据在概念化中是否使用模糊属性或模糊角色,将这些概念明确地分为清晰概念或模糊概念。 Pawlak引入的粗糙集概念用于衡量模糊概念以及模糊角色的可满足程度。通过这种方法,重新设计现有本体以应对世界上不确定的方面的推理的成本是最小的。

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