Fuzzy Clustering for Semantic Web Services Discovery based on Ontology

Web services as the most important event in distributed computing, have achieved great popularity among software developers today. A critical step in the process of developing service-oriented applications is web service discovery, i.e., the identification of existing relevant web services that can potentially be used in the context of a new web application. In this paper, we have proposed a novel method based on data mining techniques to assist and improve the web service discovery process as well as the development of service-oriented applications. Our assistant discovery approach is based on automatic finding of semantic similarity between web services through the application of clustering methods. We have introduced a new fuzzy semantic clustering algorithm which assists web service consumers in discovering a group of similar web services through an individual query. This objective is attained by way of a search space reduction mechanism which adds to the efficiency of the approach. Our proposed approach provides dynamic and flexible clusters which can be changed at discovery process. We have conducted an experimental study on a data set of tagged web services with ontology. The ontology supports the semantic analysis. Preliminary results from clustering indicate the possibility of retrieving web services at the discovery process with reasonable precision by applying the proposed similarity model. From these promising results, we conclude that web service discovery process could be performing in a reasonable time because of reduced search space.

Keywords:

Fuzzy Clustering , Ontology , Data Mining , Semantic , Web Service Discovery