Review of Machine Learning Approaches to Semantic Web Service Discovery

Abstract

A Web service can discover and invoke any service anywhere on the Web, independently of the language, location, machine, or other implementation details. The goal of Semantic Web Services is the use of richer, more declarative descriptions of the elements of dynamic distributed computation including services, processes, message-based conversations, transactions, etc. In recent years text mining and machine learning have been efficiently used for automatic classification and labeling of documents. Various Web service discovery frameworks are applying machine learning techniques like clustering, classification, association rules, etc., to discover the services semantically. This paper provides an exhaustive review of machine learning approaches used for Web Services discovery and frameworks developed based on these approaches. A thorough analysis of existing frameworks for semantic discovery of Web Services is provided in the paper.