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Parallel materialization of large ABoxes

  • Ohio State University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

This paper is concerned with the efficient computation of materialization in a knowledge base with a large ABox. We present a framework for performing this task on a shared-nothing parallel machine. The framework partitions TBox and ABox axioms using a min-min strategy. It utilizes an existing system, like SwiftOWLIM, to perform local inference computations and coordinates exchange of relevant information between processors. Our approach is able to exploit parallelism in the axioms of the TBox to achieve speedup in a cluster. However, this approach is limited by the complexity of the TBox. We present an experimental evaluation of the framework using datasets from the Lehigh University Benchmark (LUBM).

Original languageEnglish
Title of host publication24th Annual ACM Symposium on Applied Computing, SAC 2009
Pages1257-1261
Number of pages5
DOIs
StatePublished - 2009
Event24th Annual ACM Symposium on Applied Computing, SAC 2009 - Honolulu, HI, United States
Duration: Mar 8 2009Mar 12 2009

Publication series

NameProceedings of the ACM Symposium on Applied Computing

Conference

Conference24th Annual ACM Symposium on Applied Computing, SAC 2009
Country/TerritoryUnited States
CityHonolulu, HI
Period03/8/0903/12/09

Keywords

  • ABox
  • Large datasets
  • Materialization
  • Ontologies
  • Ontology
  • Parallel

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