Data-Centric Schema Creation for RDF

Abstract

Very recently, the vision of the Semantic Web has brought about new challenges in data management. One fundamental research issue in this arena is storage of the Resource Description Framework (RDF): the data model at the core of the Semantic Web. In this paper, we study a data-centric approach for storage of RDF in relational databases. The intuition behind our approach is that each RDF dataset requires a tailored table schema that achieves efficient query processing by (1) reducing the need for joins in the query plan and (2) keeping null storage below a given threshold. Using a basic structure derived from the RDF data, we propose a two-phase algorithm involving clustering and partitioning. The clustering phase aims to reduce the need for joins in a query. The partitioning phase aims to optimize storage of extra (i.e., null) data in the underlying relational database. Furthermore, our approach does not assume query workload statistics. Extensive experimental evidence using three publicly available real-world RDF data sets (i.e., DBLP, DBPedia, and Uniprot) shows that our schema creation technique provides superior query processing performance compared to previous state-of-the art approaches.