Improving Fuzzy Service Matchmaking through Concept Matching Discovery

The evolution of the Semantic Web promises infrastructures for the semantic interoperability of Web Services. Hindrances in the service discovery, composition and execution are often of syntactic nature: the difficulty in the interpretation of inputs, outputs or other nontrivial statements does not favor to find eligible advertised services which appropriately meet the consumer´s demand. This paper deals with the semantic matchmaking focusing on the ontology mismatch problem: concepts appearing in the services description are compared at semantic level in order to profit by the semantic similarity existing among entity classes (i.e. concepts). The approach is based on a multi-agent architecture and exploits fuzzy techniques to represent the multi-granular capabilities of a web service. The semantic similarity among concepts supports the clustering of the advertised services and improves the quality of the retrieved results, given a request.