document: Efficiently querying rdf data in triple stores Efficiently querying RDF data is being an important factor in applying Semantic Web technologies to real-world applications . In this context , many efforts have been made to store and query RDF data in relational database using particular schemas . In this paper , we propose a new scheme to store , index , and query RDF data in triple stores . Graph feature of RDF data is taken into considerations which might help reduce the join costs on the vertical database structure . We would partition RDF triples into overlapped groups , store them in a triple table with one more column of group identity , and build up a signature tree to index them . Based on this infrastructure , a complex RDF query is decomposed into multiple pieces of sub-queries which could be easily filtered into some RDF groups using signature tree index , and finally is evaluated with a composed and optimized SQL with specific constraints . We compare the performance of our method with prior art on typical queries over a large scaled LUBM and UOBM benchmark data ( more than 10 million triples ) . For some extreme cases , they can promote 3 to 4 orders of magnitude .
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