Abstract
Web service discovery is a challenging task due to the widespread availability of published services on the web. In this paper, a service crawler-based web service discovery framework is proposed, that employs information retrieval techniques to effectively retrieve available, published service descriptions. Their functional semantics is extracted for similarity computation and tag generation using natural language processing techniques. The framework is inherently dynamic in nature as new service descriptions may be continually added during periodic crawler runs or existing ones may be removed if service is unavailable. To deal with these issues, a dynamic, incremental clustering approach based on bird flocking behaviour is proposed. Experimental results show that semantic analysis and automatic tagging captured the services' functional semantics in a meaningful way. The algorithm effectively handled the dynamic requirements of the proposed framework by eliminating cluster recomputation overhead and achieved a speed-up factor of 61.8% when compared to hierarchical clustering.
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