Optimizing SPARQL Queries Using Graph Traversal A lgorithm

Large amounts of interlinked semantic data are available for the semantic web. Linked open data are increasing exponentially on the web. This enables users to retrieve quality and complex information from the web, which include searching and querying linked data. World Wide Web Consortium has standardized SPARQL as a query language for the semantic linked open data. Current SPARQL query processing is still on its early stages for both scalability and efficiency. There are several approaches have been proposed but only few are implemented. This approach proposes the use of graph traversal algorithms for the optimization of SPARQL query. SPARQL query can be optimized using this approach in two phases. In first phase, we proposed to generate a query execution plan using graph traversal algorithm. Second phase focuses on to reduce the search space and optimize. To optimize the query execution, individuals which are not connected to any other individual are removed with acceptable loss of knowledge base. It represents a generic representation of graph for any ontology. The query is executed again on the revised graph which will have reduced query execution time with acceptable loss of knowledge base.