Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A Bayesian model of plan recognition
Artificial Intelligence
The String-to-String Correction Problem
Journal of the ACM (JACM)
Learning to map between ontologies on the semantic web
Proceedings of the 11th international conference on World Wide Web
PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Comparison of Schema Matching Evaluations
Revised Papers from the NODe 2002 Web and Database-Related Workshops on Web, Web-Services, and Database Systems
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Ontology Matching
Managing Uncertainty in Schema Matcher Ensembles
SUM '07 Proceedings of the 1st international conference on Scalable Uncertainty Management
OMEN: a probabilistic ontology mapping tool
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
A unified approach to matching semantic data on the Web
Knowledge-Based Systems
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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.