Abstract
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.
The work presented in this paper has been funded in part by Science Foundation Ireland under Grant No. SFI/08/CE/I1380 (Lion-2), by the EU MOST project, the EPSRC LITRO project, and by an IRCSET Scholarship.
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Hogan, A., Pan, J.Z., Polleres, A., Decker, S. (2010). SAOR: Template Rule Optimisations for Distributed Reasoning over 1 Billion Linked Data Triples. In: Patel-Schneider, P.F., et al. The Semantic Web – ISWC 2010. ISWC 2010. Lecture Notes in Computer Science, vol 6496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17746-0_22
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