Abstract: 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.