Fuzzy Matchmaking Based On Semantic Web Services

Web Services have become increasingly popular in research communities and industry. In order to realize full potentials of Web Services, the study of discovery and composition for Web Services becomes an essential task. One of the core mechanisms in the development of discovery and composition is matchmaking which enables service consumers (user of spftware process) to locate and compose the required services provided by service providers. Matchmaking is considered as a search or discovery problem in a bounded space. It takes service consumers'requests and a collection of services from services providers as input via its matchmaker mechanism to identify a list of best matched paries. In the context of Web Services matchmaking, the representation of services and selection of searching crititeria are the critical factors to determine the quality of the output.Current matchmaking techniques of Web Services is based on UDDI, which is realized by exactly matching. However,it can't well support fuzzy matchmaking based on probability and restriction and deal with reasoning with fuzzy regular, which affects the executing of the Web Services. As well, the classify of UDDI is based on NACIIC which wastes lot of time. So it can't well satisfy the consumers'demand.This paper adds a new mechanism dealing with the services fuzzy properties on the base of current tapitical Semantic Web Services. It is composed of a full fuzzy rules by introducing fuzzy set theory, fuzzy predicate and with natural language. And we complete fuzzy matchmaking of rules through semantic distance. We use fuzzy logic to classify and present the vague or imprecise data at abstract. Fuzzy logic enables data representation with linguistic variables and fuzzy values. The aim is to increase the efficiency of the discovery of Web Services and reduce the searching space. The introduction of fuzzy logic, the view was used to quantify the direct results. In order to resolve different opinions among service consumers and providers, the Similarity Aggregation Method (SAM) is adopted. SAM is the method that can aggregate different experts'and users'fuzzy opinion to reach a group fuzzy consensus opinion. It can reduce subjective factors.Finally, a fuzzy Web Services matchmaking arithmetic is presented. Experimentation result shows FMD has more precision ratio relative to the other three methods.