Markov network based ontology matching

Sivan Albagli, Rachel Ben-Eliyahu-Zohary, Solomon E. Shimony

Research output: Contribution to journalArticlepeer-review

40 Scopus citations

Abstract

Ontology matching is a vital step whenever there is a need to integrate and reason about overlapping domains of knowledge. Systems that automate this task are of a great need. iMatch is a probabilistic scheme for ontology matching based on Markov networks, which has several advantages over other probabilistic schemes. First, it handles the high computational complexity by doing approximate reasoning, rather then by ad-hoc pruning. Second, the probabilities that it uses are learned from matched data. Finally, iMatch naturally supports interactive semi-automatic matches. Experiments using the standard benchmark tests that compare our approach with the most promising existing systems show that iMatch is one of the top performers.

Original languageEnglish
Pages (from-to)105-118
Number of pages14
JournalJournal of Computer and System Sciences
Volume78
Issue number1
DOIs
StatePublished - 1 Jan 2012

Keywords

  • Markov networks
  • Ontology matching
  • Probabilistic reasoning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Networks and Communications
  • Computational Theory and Mathematics
  • Applied Mathematics

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