Web Services Discovery in Metric Space through Similarity Search

Most current semantic web services discovery approaches focus on the matchmaking of services in a specific description language such as OWL-S, and WSML. However, in practical applications, effective services discovery is expected to have the ability to deal with all heterogeneous and distributed web services. This paper proposes a novel semantic web service discovery method using the metric space approach to resolve this problem. In the method, all heterogeneous web services are modeled as similar metric objects regardless of concrete description languages, and thereby the discovery problem can be treated as similarity search in metric space with a uniform criterion. In the matchmaking process, both the functional semantics and non-functional semantics of the web services are integrated as selection conditions for similarity query. And two types of similarity queries: range query and an improved nearest neighbor query are combined to produce a sorted result set so that the method can be better applied to practical situation.