SAOR: template rule optimisations for distributed reasoning over 1 billion linked data triples

  • Authors:

  • Aidan Hogan;Jeff Z. Pan;Axel Polleres;Stefan Decker

  • Affiliations:

  • Digital Enterprise Research Institute, National University of Ireland, Galway;Dpt. of Computing Science, University of Aberdeen;Digital Enterprise Research Institute, National University of Ireland, Galway;Digital Enterprise Research Institute, National University of Ireland, Galway

  • Venue:
  • ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
  • Year:
  • 2010

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Abstract

In this paper, we discuss optimisations of rule-based materialisation approaches for reasoning over large static RDF datasets. We generalise and reformalise 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.