| The Semantic Web is an extension of the current Web, in which Web resources are given computer-understandabale semantics, better enabling computers and people to work in cooperation. One of the declared goals of the Semantic Web is to reason about knowledge and data which are pervaded on the Web. Therefore, how to represent these knowledge and data and how to reason with them have become very important research topics of the Semantic Web. As an important tool for representation and reasoning of knowledge, rules possess many features including high expressiveness, terseness, easy understanding and requiring small memory space, which let rules play important roles in many areas including Artificial Intelligence, and have been applied to markup the constructions of E-Business and the Semantic Web.With the development of rules in the Semantic Web, rule languages came into being successively. However, these rule languages cannot represent much fuzzy knowledge existing in the Semantic Web. In recent years, the fuzzy set theory proposed by Zadeh is employed to represent the fuzziness of the Semantic Web, and thus fuzzy rule languages in the Semantic Web came into being. Besides fuzzniess, the Semantic Web rules also have another feature:nonmonotonicity, which is caused by the incompleteness of knowledge. How to represent and reason about these fuzzy and nonmonotonic knowledge has been firmly put on the agenda of the Semantic Web. In addition, with the widespread application of heterogeneous fuzzy nonmonotonic rule languages and rule systems in areas including Web Services, fuzzy rule interchange becomes an important issue to be solved.Researches on fuzzy rule languages and rule interchange in the Semantic Web have obtained some achievements, and at the same time, RIF (Rule interchange format) has become a recommendation of the W3C (WWW Consortium) on rule interchange. However, it should be pointed out that, the existing fuzzy rule languages in the Semantic Web are not capable of representing fuzzy and nonmonotonic knowledge simultaneously, the current rule interchange isn’t centerd on RIF, and there have been few efforts at investigating fuzzy rule interchange and its standard format.Proceeding from the representation of fuzzy knowledge in the Semantic Web, based on the fuzzy set theory, possibility distribution theory and vague set theory, this thesis proposes several Semantic Web fuzzy rule languages capable of representing and reasoning with fuzziness and nonmonotonicity, the RIF is fuzzified, fuzzy rule interchange format f-RIF (fuzzy RIF) is proposed, and based on the above, a fuzzy rule interchange architecture centered on f-RIF is constructed. The innovative contributions of this thesis are as follows.(ⅰ) Aiming at the fact that crisp rule languages don’t suffice to represent a great deal of fuzzy and nonmonotonic knowledge in the Semantic Web, four fuzzy rule languages are proposed. Firstly, negation and negation as failure are introduced into fuzzy rule languages, and f-NSWRL (fuzzy Nonmonotonic Semantic Web Rule Language) is proposed to represent nonmonotonic fuzzy knowledge; next, on the basis of the vague set theory, Vague-SWRL employing intervals to more precisely express fuzzy knowledge is proposed, a new concept second degree weight is presented, and the semantics and the XML Schema of Vague-SWRL are discussed and defined, respectively; then, based on research fruits in Artificial Intelligence, if-then rules are introduced into the Semantic Web, and fuzzy rule language f-SW-if-then-RL (fuzzy Semantic Web if-then Rule Language) is proposed; finally, combining unless rules with f-SW-if-then-RL, f-SW-if-then-unless-RL (fuzzy Semantic Web if-then-unless Rule Language) which can simutaneously represent fuzzy and nonmonotonic knowledge is proposed, and its syntax, semantics and XML Schema are defined,(ⅱ) Aiming at the fact that the RIF is not capable of representing and interchanging fuzzy rules, f-RIF, a fuzzy rule interchange format, is proposed. At first, the f-RIF metamodel including fuzzy terms, fuzzy formulas, fuzzy classes, fuzzy properties, fuzzy restrictions and fuzzy rule implications is defined from the view of visually modelling; then, based on the given f-RIF presentation syntax, a UML Profile for f-RIF fuzzy rules is also constructed; after that, the XML Schema of f-RIF is defined to represent the concrete syntax of f-RIF; at last, based on the fuzzy set theory and the extended set of truth values, the model-theoretic semantics of the f-RIF is defined.(ⅲ) To deal with the problem of communications of fuzzy rules among heterogeneous systems, a fuzzy interchange architecture f-RIA (fuzzy Rule Interchange Architecture) is constructed. Firstly, centered on the f-RIF metamodel and the metamodels of f-SWRL, f-NSWRL, Vague-SWRL, RuleML, f-SW-if-then-RL and f-SW-if-then-unless-RL, fuzzy rule interchange structure f-RIA is constructed, and abstract syntaxes transformations between the above seven languages and the transformations between abstract syntaxes and concrete syntaxes of the seven languages are implemented; next, the issue of information loss occurring in the process of rule interchange is discussed, and the corresponding remedial measures are given to reduce the adverse influences caused by information loss; finally, based on the f-RIA and on the Eclipse platform, a prototype system f-RIA1.0interchanging fuzzy rules is designed and implemented. |
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