Vague-SWRL: A Fuzzy Extension of SWRL

Authors:
Xing Wang;Z. M. Ma;Li Yan;Xiangfu Meng
Affiliations:
College of Information Science and Engineering, Northeastern University, Shenyang, China 110004;College of Information Science and Engineering, Northeastern University, Shenyang, China 110004;College of Information Science and Engineering, Northeastern University, Shenyang, China 110004;College of Information Science and Engineering, Northeastern University, Shenyang, China 110004
Venue:
RR '08 Proceedings of the 2nd International Conference on Web Reasoning and Rule Systems
Year:
2008

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Abstract

Rules in the Web have become a mainstream topic since inference rules are marked up for e-commerce and are identified as a design issue of the semantic web [7]. SWRL [4] is incapable of representing the imprecision and uncertainty, and a single membership degree in fuzzy sets [6] is inaccurate to represent the imprecise knowledge. Based on vague sets [2] which employ membership degree intervals to represent fuzzy information, we propose a fuzzy extension of SWRL(vague-SWRL. What's more, weights in f-SWRL [5] have no power to represent the importance of membership degrees. In order to modify the membership degrees and to balance and supplement the weights of vague classes and properties (i.e., first degree weights), we present the notion of second degree weight to represent weights of the membership degrees in vague-SWRL. In addition, we extend RuleML to express vague-SWRL.