Extensible soft semantic web services agent

Web services technology is critical for the success of business integration and other application fields such as bioinformatics. However, there are two challenges facing the practicality of Web services: (a) efficient location of the Web service registries that contain the requested Web services and (b) efficient retrieval of the requested services from these registries with high quality of service (QoS). The main reason for this problem is that current Web services technology is not semantic-oriented. Several proposals have been made to add semantics to Web services to facilitate discovery and composition of relevant Web services. Such proposals are being referred to as semantic Web services (SWS). However, most of these proposals do not address the second problem of retrieval of web services with high QoS. In this paper, we propose a framework called soft semantic Web services agent (soft SWS agent) for providing high QoS SemanticWeb services using soft computing methodology. Since different application domains have different requirement for QoS, it is impractical to use classical mathematical modeling methods to evaluate the QoS of semantic Web services. We use fuzzy neural networks with Genetic Algorithms (GA) as our study case. Simulation results show that the soft computing methodology is practicable to handle fuzzy and uncertain QoS metrics effectively.