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RDFox: A Highly-Scalable RDF Store

Part of the Lecture Notes in Computer Science book series (LNISA,volume 9367)

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 highly-efficient 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.

Keywords

  • Resource Description Framework
  • Indexing Scheme
  • SPARQL Query
  • Triple Pattern
  • Query Answering

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Correspondence to Yavor Nenov .

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Nenov, Y., Piro, R., Motik, B., Horrocks, I., Wu, Z., Banerjee, J. (2015). RDFox: A Highly-Scalable RDF Store. In: , et al. The Semantic Web - ISWC 2015. ISWC 2015. Lecture Notes in Computer Science(), vol 9367. Springer, Cham. https://doi.org/10.1007/978-3-319-25010-6_1

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  • DOI: https://doi.org/10.1007/978-3-319-25010-6_1

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