AAAI Publications, Twenty-First International Joint Conference on Artificial Intelligence

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Markov Network based Ontology Matching
Sivan Gali Albagli, Solomon Eyal Shimony, Rachel Ben-Eliyahu-Zohary

Last modified: 2009-06-26

Abstract


iMatch is a probabilistic scheme for ontology matching based on Markov networks, which has several advantages over other probabilistic schemes. First, it uses undirected networks, which better supports the non-causal nature of the dependencies. Second, it handles the high computational complexity involved by approximate reasoning, rather then by ad-hoc pruning. Third, 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.

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