Parallelization of Query Processing over Expressive Ontologies

E. Patrick Shironoshita, Da Zhang, Mansur R. Kabuka, Jia Xu

Research output: Contribution to journalConference article

Abstract

Efficient query answering over Description Logic (DL) ontologies with very large datasets is becoming increasingly vital. Recent years have seen the development of various approaches to ABox partitioning to enable parallel processing. Instance checking using the enhanced most specific concept (MSC) method is a particularly promising approach. The applicability of these distributed reasoning methods to typical ontologies has been shown mainly through anecdotal observation. In this paper, we present an analysis method that makes use of random graph theory to show that the enhanced MSC method results in very small, tractable concepts provided that the number of role assertions removed from consideration is large enough. We also present execution time and efficiency of a parallel implementation deployed over computing clusters of various sizes, showing the ability of the method to process instance checking for large scale datasets.

Original languageEnglish (US)
Pages (from-to)116-126
Number of pages11
JournalCEUR Workshop Proceedings
Volume2029
StatePublished - 2017
Event4th Annual International Symposium on Information Management and Big Data, SIMBig 2017 - Lima, Peru
Duration: Sep 4 2017Sep 6 2017

ASJC Scopus subject areas

  • Computer Science(all)

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  • Cite this

    Patrick Shironoshita, E., Zhang, D., Kabuka, M. R., & Xu, J. (2017). Parallelization of Query Processing over Expressive Ontologies. CEUR Workshop Proceedings, 2029, 116-126.