Optimising ontology stream reasoning with truth maintenance system

Yuan Ren*, Jeff Z. Pan

*Corresponding author for this work

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

55 Citations (Scopus)

Abstract

So far researchers in the Description Logics / Ontology communities mainly consider ontology reasoning services for static ontologies. The rapid development of the Semantic Web and its emerging data ask for reasoning technologies for dynamic knowledge streams. Existing work on stream reasoning is focused on lightweight languages such as RDF and RDFS. In this paper, we introduce the notion of Ontology Stream Management System (OSMS) and present a stream-reasoning approach based on Truth Maintenance System (TMS). We present optimised EL++ algorithm to reduce memory consumption. Our evaluations show that the optimisation improves TMS-enabled EL++ reasoning to deal with relatively large volumes of data and update efficiently.

Original languageEnglish
Title of host publicationCIKM'11 - Proceedings of the 2011 ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery (ACM)
Pages831-836
Number of pages6
ISBN (Print)9781450307178
DOIs
Publication statusPublished - 13 Dec 2011
Event20th ACM Conference on Information and Knowledge Management, CIKM'11 - Glasgow, United Kingdom
Duration: 24 Oct 201128 Oct 2011

Conference

Conference20th ACM Conference on Information and Knowledge Management, CIKM'11
CountryUnited Kingdom
CityGlasgow
Period24/10/1128/10/11

Keywords

  • ontology
  • stream reasoning
  • truth maintenance system

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

Fingerprint Dive into the research topics of 'Optimising ontology stream reasoning with truth maintenance system'. Together they form a unique fingerprint.

  • Cite this

    Ren, Y., & Pan, J. Z. (2011). Optimising ontology stream reasoning with truth maintenance system. In CIKM'11 - Proceedings of the 2011 ACM International Conference on Information and Knowledge Management (pp. 831-836). Association for Computing Machinery (ACM). https://doi.org/10.1145/2063576.2063696