Semi - Automatically Annotating Semantic Web Services ( Extended

The semantic Web Services vision requires that each service be annotated with semantic metadata. Various metadata languages (such as OWL-S (DAML-S Coalition 2003)) have been proposed to fill this “semantic gap”. However, manually creating such metadata is tedious and error-prone. Software engineers, accustomed to tools that automatically generate WSDL, might not want to invest the required effort. This extended abstract describes ASSAM, a tool that assists a user in creating semantic metadata for Web Services. ASSAM’s capabilities to automatically create semantic metadata are supported by two machine learning algorithms. First, we have developed an iterative relational classification algorithm for semantically classifying Web Services, their operations, and input and output messages. Second, to aggregate the data returned by multiple semantically related Web Services, we have developed a schema mapping algorithm based ensembles of string distance metrics. The remainder of this paper is organized as follows: (1) 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. (2) We describe and evaluate the iterative relational classification algorithm that provides these suggestions. (3) We describe and evaluate OATS, a schema mapping algorithm specifically designed for the Web Services context. This extended abstract merely summarizes our efforts; for details and a discussion of related work, see (Heß & Kushmerick 2004; Johnston & Kushmerick 2004; Heß, Johnston, & Kushmerick 2004).