语义Web规则语言的模糊扩展

A Fuzzy Extension of Semantic Web Rule Language

语义Web规则语言SWRL有较强的表达能力.但它无法表示语义Web中含有的大量不精确和不确定的知识和信息;模糊集中单个隶属度不能准确表达模糊信息;f-SWRL中的权重只能表达模糊类和模糊属性的重要程度.针对以上问题,提出了一种基于vague集的模糊SWRL的扩展形式vague SWRL,给出了二级权重的概念来修饰和限定模糊类和模糊属性的隶属度,研究了vague-SWRL的语法语义,给出了vague-SWRL的规则实例.Vague集的引入,特别是二级权重的引入,增强了模糊规则的表达能力,符合语义Web的发展趋势,具有较大的优越性.

Although SWRL, i.e. semantic Web rule language, has highly expressive power, it is unable to express the imprecise and uncertain knowledge/information which is so much in semantic Web. In addition, a single membership degree in fuzzy sets is inaccurate to express the fuzzy information, and the weights in f-SWRL can only express the importance of fuzzy classes and fuzzy properties. A fuzzy SWRL extension named vague-SWRL is therefore proposed on the basis of vague sets, with the notion of second-degree weight introduced to modify and restrict the membership degrees of fuzzy classes and fuzzy properties. The syntax and semantics of vague- SWRL are investigated and specified, and a rule example is given to illustrate the features of vague-SWRL. Introducing the vague sets especially the second-degree weights into rule languages, the expressive power of vague rules is enhanced so as to conform to the developmental trend of semantic Web with significant superiority.