SAOR: Template Rule Optimisations for Distributed Reasoning over 1 Billion Linked Data Triples

  • Aidan Hogan
  • Jeff Z. Pan
  • Axel Polleres
  • Stefan Decker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6496)


In this paper, we discuss optimisations of rule-based materialisation approaches for reasoning over large static RDF datasets. We generalise and re-formalise what we call the “partial-indexing” approach to scalable rule-based materialisation: the approach is based on a separation of terminological data, which has been shown in previous and related works to enable highly scalable and distributable reasoning for specific rulesets; in so doing, we provide some completeness propositions with respect to semi-naïve evaluation. We then show how related work on template rules – T-Box-specific dynamic rulesets created by binding the terminological patterns in the static ruleset – can be incorporated and optimised for the partial-indexing approach. We evaluate our methods using LUBM(10) for RDFS, pD* (OWL Horst) and OWL 2 RL, and thereafter demonstrate pragmatic distributed reasoning over 1.12 billion Linked Data statements for a subset of OWL 2 RL/RDF rules we argue to be suitable for Web reasoning.


Resource Description Framework Link Data Graph Pattern Rule Application Partial Indexing 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Aidan Hogan
    • 1
  • Jeff Z. Pan
    • 2
  • Axel Polleres
    • 1
  • Stefan Decker
    • 1
  1. 1.Digital Enterprise Research InstituteNational University of IrelandIreland
  2. 2.Dpt. of Computing ScienceUniversity of AberdeenU.K.

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