Efficiently querying RDF data in triple stores

Efficiently querying RDF [1] 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)in [3]. For some extreme cases, they can promote 3 to 4 orders of magnitude.