Abstract
We present RDFox—a main-memory, scalable, centralised RDF store that supports materialisation-based parallel datalog reasoning and SPARQL query answering. RDFox uses novel and highlyefficient parallel reasoning algorithms for the computation and incremental update of datalog materialisations with efficient handling of owl: sameAs. In this system description paper, we present an overview of the system architecture and highlight the main ideas behind our indexing data structures and our novel reasoning algorithms. In addition, we evaluate RDFox on a high-end SPARC T5-8 server with 128 physical cores and 4TB of RAM. Our results show that RDFox can effectively exploit such a machine, achieving speedups of up to 87 times, storage of up to 9.2 billion triples, memory usage as low as 36.9 bytes per triple, importation rates of up to 1 million triples per second, and reasoning rates of up to 6.1 million triples per second.
Cite
CITATION STYLE
Nenov, Y., Piro, R., Motik, B., Horrocks, I., Wu, Z., & Banerjee, J. (2015). RDFox: A highly-scalable RDF store. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9367, pp. 3–20). Springer Verlag. https://doi.org/10.1007/978-3-319-25010-6_1
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