TY - GEN
T1 - Markov network based ontology matching
AU - Albagli, Sivan
AU - Ben-Eliyahu-Zohary, Rachel
AU - Shimony, Solomon E.
PY - 2009/1/1
Y1 - 2009/1/1
N2 - 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 by doing approximate reasoning, rather then by ad-hoc pruning. Third, the probabilities that it uses are learned from matched data. Finally, iMatch naturally supports interactive semiautomatic 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.
AB - 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 by doing approximate reasoning, rather then by ad-hoc pruning. Third, the probabilities that it uses are learned from matched data. Finally, iMatch naturally supports interactive semiautomatic 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.
UR - https://www.scopus.com/pages/publications/77958563160
M3 - Conference contribution
AN - SCOPUS:77958563160
SN - 9781577354260
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 1884
EP - 1889
BT - IJCAI-09 - Proceedings of the 21st International Joint Conference on Artificial Intelligence
PB - International Joint Conferences on Artificial Intelligence
T2 - 21st International Joint Conference on Artificial Intelligence, IJCAI 2009
Y2 - 11 July 2009 through 16 July 2009
ER -