中文摘要：

近年来,使用规则表示和推理知识成为语义Web的重要研究议题之一.If-then规则已经广泛应用于人工智能领域.但是,由于缺乏语义Web元素、非单调元素和模糊元素,if-then规则无法表示和推理语义Web中广泛存在的、非单调模糊知识.为了解决此问题,结合if-then规则和OWL DL（Web Ontology Language Description Logic）,在模糊集和可能性理论的框架下,提出两种新的语义Web规则语言—f-SW-if-then-RL（fuzzy Semantic Web if-then Rule Language）和f-SW-if-then-unless-RL（fuzzySemantic Web if-then-unless Rule Language）,定义它们的语法和XML Schema,研究它们的语义.提出的两种规则语言能够增强规则语言表示和推理语义Web非单调模糊知识的能力.

英文摘要：

In recent years,using rules to represent and reason with knowledge has become an important research issue in the Semantic Web.If-then rules have been widely studied and applied in Artificial Intelligence community for many years.However,due to lack of the Semantic Web elements,nonmonotonic elements and fuzzy elements,they fail to represent and reason with nonmonotonic fuzzy knowledge pervaded in the Semantic Web.To solve this problem,we combine if-then rules with OWL DL（Web Ontology Language Description Logic） in the framework of fuzzy sets and possibility theory,and propose two new Semantic Web rule languages—f-SW-if-then-RL（fuzzy Semantic Web if-then Rule Language） and f-SW-if-then-unless-RL（fuzzy Semantic Web if-then-unless Rule Language）,defining their Abstract syntaxes and XML Schema,and investigating their semantics.The two new languages enhance the ability of rule languages in the respect of representing and reasoning with fuzzy and nonmonotonic Semantic Web knowledge.