Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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
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
One size does not fit all: customizing ontology alignment using user feedback
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Probabilistic-logical web data integration
RW'11 Proceedings of the 7th international conference on Reasoning web: semantic technologies for the web of data
Expanding knowledge source with ontology alignment for augmented cognition
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Statistical relational data integration for information extraction
RW'13 Proceedings of the 9th international conference on Reasoning Web: semantic technologies for intelligent data access
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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 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.