Semi-Automatically Annotating Semantic Web Services (Extended Abstract)
Andreas Heß, Eddie Johnston, Nicholas Kushmerick · 2004
Andreas He Eddie Johnston Nicholas Kushmerick Computer Science Department, University College Dublin, Ireland {andreas.hess, eddie.johnston, nick}@ucd.ie Overview 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.