Machine learning techniques for annotating semantic web services

The vision of semantic Web Services is to provide the means for fully automated discovery, composition and invocation of loosely coupled software components. One of the key efforts to address this “semantic gap” is the well-known OWLS ontology (The DAML Services Coalition 2003). However, software engineers who are developing Web Services usually do not think in terms of ontologies, but rather in terms of their programming tools. Existing tools for both the Java and .NET environments support the automatic generation of WSDL. We believe that it would boost the semantic service web if similar tools existed to (semi-) automatically generate OWL-S or a similar form of semantic metadata. In this paper we will present a tool called ASSAM— Automated Semantic Service Annotation with Machine Learning—that addresses these needs. ASSAM consists of two parts, a WSDL annotator application, and OATS, a data aggregation algorithm. First, we describe the WSDL annotator application. This component of ASSAM uses machine learning to provide the user with suggestions on how to annotate the elements in the WSDL. In go on to describe the iterative relational classification algorithm that provides these suggestions. We evaluate our algorithms on a set of 164 Web Services.1 Second, we describe OATS, a novel schema mapping algorithm specifically designed for the Web Services context, and empirically demonstrate its effectiveness on 52 invokable Web Service operations. OATS addresses the problem of aggregating the heterogenous data from several Web Services.