איל שמעוני

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.