Abstract
The Resource Description Framework (RDF) is a flexible model for representing information about resources in the web. With the increasing amount of RDF data which is becoming available, efficient and scalable management of RDF data has become a fundamental challenge to achieve the Semantic Web vision. The RDF model has attracted a lot of attention of the database community and many researchers have proposed different solutions to store and query RDF data efficiently. In this paper, we focus on evaluating the state-of-the-art of the approaches which are relying on the relational infrastructure to provide scalable engines to store and query RDF data. Our experimental evaluation is done on top of recently introduced SP2Bench performance benchmark for RDF query engines. The results of our experiments shows that there is still room for optimization in the proposed generic relational RDF storage schemes and thus new techniques for storing and querying RDF data are still required to bring forward the Semantic Web vision.
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