Soft Semantic Web services agent

Abstract

Web services play an active role in the business integration and other fields such as bioinformatics. Current Web services technologies such as WSDL, UDDI, BPEL4WS and BSML are not semantic-oriented. Several proposals have been proposed to develop Semantic Web services to facilitate the discovery of relevant Web services. In our vision, with the mature of Semantic Web services technologies, there will be a lot of public or private Semantic Web services Registries based on specific ontologies. These Registries may provide a lot of similar Web services. So how to provide the high quality of service (QoS) Semantic Web services for specific domain using these Registries will be a challenge task. Different domains have different requirements of QoS, it is impractical to use classical mathematical modeling methods to evaluate the QoS of Semantic Web services. In this paper, we propose a framework called Soft Semantic Web services Agent (SSWSA) for providing high QoS Semantic Web services using soft computing methodology. And we will use fuzzy neural network with GA learning algorithm as our study case. Simulation result shows that the SSWSA could handle fuzzy and uncertain QoS metrics effectively.