Efficient Query Processing of Semantic Data Using Graph Contraction on RDBMS

Efficient store and query of RDF graph database is of increasing importance due to the popularity and widespread acceptance of RDF on various applications including semantic/situation aware computing. In this paper, we have applied an query optimization based on graph contraction that boosts the query processing on relationally-backed RDF store. The query optimization technique based on graph contraction creates summarized graph in advance and uses it to efficiently query the original dataset. We used decomposition storage model on top of RDBMS to represent RDF graph, and applied the optimization technique based on graph contraction. Preliminary experiments using MySQL and synthetic dataset showed that the method can improve the query performance by 6.62 times. This means that the contraction based optimization on RDBMS is promising technique for retrieving data in semantic/situation aware computing.