Conjunctive Query for Linked Data Based on Semi-Join

As the semantic web develops, a large amount of available web data grows rapidly. It is still a hot topic how to query useful information from these Web data more quickly, efficiently and exactly. To approach the issues, we present a conjunctive query method for Linked Data based on semi-join in order to reduce the overhead to transit the intermediate results produced by each data source from the network and when selecting data source, we exploit the query results of the basic graph pattern to precompute the cardinalities of the intermediate results and present a new data source selection algorithm based on "vocabulary of interlinked dataset (voiD) "+" SPARQL ASK" to improve the accuracy of the data source selection. To reduce the query response time, we present a parallel semi-join algorithm. Extensive experiments show that our solution is more efficient and more effective compared to the existing techniques.