Optimize SPARQL by Combining Semantic Reduction and Selectivity Estimation
Ouyang Dang-tong · Dianzi xuebao · 2010
Base on defining SPARQL query Optimization for RDF data,the semantic reduction approach is proposed.It’s aimed to reduce basic graph patterns according to the semantic relationships between ontology concepts.In further,a novel algorithm,named RS-Opti,which combines the semantic reduction approach and the selectivity estimation approach is presented.RS-Opti algorithm is estimated by LUBM Benchmark.The result shows that RS-Opti algorithm is better than using semantic reduction or selectivity estimation alone.It is advanced in comparison with other SPARQL engines,especially when SPARQL query contains more basic graph patterns and more complex semanteme.