Self-Adaptive Semantic Classification using Domain Knowledge and Web Usage Log for Web Service Discovery

The current internet has seen a tremendous growth of web services as an important technology for exchanging information, computing resources and programs online. With these increasing acceptance and existence of internet services, it has become high importance to have effective and accurate recommendation systems. It was observed that vast majority of web services are without associated with a precise description. Due to which most relevant service are not discovered to a particular user service request. Semantic based service discovery are mostly considered for service classification in the similar activities services. But these approaches are limited in their classification to the domain trained knowledge for the service discovery. This paper propose a novel self-adaptive semantic classification approach using service knowledge ontology and web user log frequent pattern in combination to improvise the web service discovery. The performance evaluation measures shows an improvisation in the classifying ability utilizing the service information of a service specific knowledge and user web log