Matching semantic web services using learning accuracy

Abstract

The automatic discovery of suitable Web services for a given task is one of the key elements in implementing the Semantic Web vision. This paper presents a new matching algorithm for Semantic Web service discovery. Our matching algorithm allows for ranking the discovered Web services according to their relevance to the service request. The learning accuracy is proposed as a suitable metric for determining the semantic similarity between a service request and the service advertisements. The semantic similarity is computed by considering the semantic information encoded in a domain ontology, including both the concept hierarchy and the properties of the concepts. Evaluating the semantic similarity between a service request and a service advertisement is based on the concepts, their semantic relations, their common and distinguishing properties, and the semantic relations between their properties. © 2009 IEEE.