Resource Selection from Distributed Semantic Web Stores

Semantic web is gaining popularity as the candidate for next generation World Wide Web. Distribution of the data across number of physical information stores and proliferation of semantic web data brings variety of non-trivial challenges. One of such challenge is to identify information stores for a given query. This paper presents a framework to address this problem probabilistically and by exchanging the summaries of actual contents. Experimental evaluation shows promising results with high recall for probabilistic approach and lower response time for pre-processed summary exchanges.