Parallelization of conjunctive query answering over ontologies

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 a parallelizable, enhanced MSC method for the answering of ABox conjunctive queries, using a set of syntactic conditions that permit querying of large practical ontologies in reasonable time, and combining it with pattern matching to answer queries over role assertions. We also present execution time and efficiency of an 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)
Title of host publicationInformation Management and Big Data - 4th Annual International Symposium, SIMBig 2017, Revised Selected Papers
PublisherSpringer Verlag
Pages1-14
Number of pages14
ISBN (Print)9783319905952
DOIs
StatePublished - Jan 1 2018
Event4th Annual International Symposium on Information Management and Big Data, SIMBig 2017 - Lima, Peru
Duration: Sep 4 2017Sep 6 2017

Publication series

NameCommunications in Computer and Information Science
Volume795
ISSN (Print)1865-0929

Other

Other4th Annual International Symposium on Information Management and Big Data, SIMBig 2017
CountryPeru
CityLima
Period9/4/179/6/17

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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  • Cite this

    Shironoshita, E. P., Zhang, D., Kabuka, M. R., & Xu, J. (2018). Parallelization of conjunctive query answering over ontologies. In Information Management and Big Data - 4th Annual International Symposium, SIMBig 2017, Revised Selected Papers (pp. 1-14). (Communications in Computer and Information Science; Vol. 795). Springer Verlag. https://doi.org/10.1007/978-3-319-90596-9_1