Closing the Service Discovery Gap by Collaborative Tagging and Clustering Techniques

Whereas the number of services that are provided online is growing rapidly, current service discovery approaches seem to have problems fulfilling their objectives. Existing approaches are hampered by the complexity of underlying semantic service models and by the fact that they try to impose a technical vocabulary to users. This leads to what we call the service discovery gap. In this paper we envision an approach that allows users to query or browse services using free text tags, thus providing an interface in terms of the users' vocabulary instead of the service’s vocabulary. Unlike simple keyword search, we envision tag clouds associated with services themselves as semantic descriptions carrying collaborative knowledge about the service that can be clustered hierarchically, forming lightweight “ontologies”. Besides tag-based discovery only describing the service on a global view, we envision refined tags and refined search/discovery in terms of the concepts that are common to all current semantic service description models, i.e. input, output, and operation. We argue that Service matching can be achieved, by applying tag-cloud-based service similarity on the one hand and by clustering services using case based indexing and retrieval techniques on the other hand.