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International Journal of Rough Sets and Data Analysis最新文献

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Reasoning on vague ontologies using rough set theory 模糊本体的粗糙集推理
Pub Date : 2021-01-01 DOI: 10.4018/ijrsda.288522
Web ontologies can contain vague concepts, which means the knowledge about them is imprecise and then query answering will not possible due to the open world assumption. A concept description can be very exact (crisp concept) or exact (fuzzy concept) if its knowledge is complete, otherwise it is inexact (vague concept) if its knowledge is incomplete. In this paper, we propose a method based on the rough set theory for reasoning on vague ontologies. With this method, the detection of vague concepts will insert into the original ontology new rough vague concepts where their description is defined on approximation spaces to be used by extended Tableau algorithm for automatic reasoning. The extended Tableau algorithm by this rough set-based vagueness is intended to answer queries even with the presence of incomplete information.
Web本体可能包含模糊的概念,这意味着关于它们的知识是不精确的,并且由于开放世界的假设,查询回答将无法实现。一个概念描述可以是非常精确的(清晰的概念)或精确的(模糊的概念),如果它的知识是完整的,否则它是不精确的(模糊的概念),如果它的知识不完整。本文提出了一种基于粗糙集理论的模糊本体推理方法。该方法通过对模糊概念的检测,在原本体中插入新的粗糙的模糊概念,并将其描述定义在近似空间上,供扩展的Tableau算法用于自动推理。这种基于粗糙集的模糊性的扩展Tableau算法旨在回答即使存在不完整信息的查询。
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International Journal of Rough Sets and Data Analysis
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