Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Learning to map between ontologies on the semantic web
Proceedings of the 11th international conference on World Wide Web
Global Viewing of Heterogeneous Data Sources
IEEE Transactions on Knowledge and Data Engineering
Generic Schema Matching with Cupid
Proceedings of the 27th International Conference on Very Large Data Bases
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
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 mapping: the state of the art
The Knowledge Engineering Review
Conceptual structuring through term variations
MWE '03 Proceedings of the ACL 2003 workshop on Multiword expressions: analysis, acquisition and treatment - Volume 18
COMA: a system for flexible combination of schema matching approaches
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
FCA-MERGE: bottom-up merging of ontologies
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
A survey of schema-based matching approaches
Journal on Data Semantics IV
Ontology change: Classification and survey
The Knowledge Engineering Review
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Most research works about ontology or schema matching are based on symmetric similarity measures. By transposing the association rules paradigm, we propose to use asymmetric measures in order to enhance matching. We suggest an extensional and asymmetric matching method based on the discovery of significant implications between concepts described in textual documents. We use a probabilistic model of deviation from independence, named implication intensity. Our method is divided into two consecutive stages: (1) the extraction in documents of relevant terms for each concept; (2) the discovery of significant implications between the concepts. Our method is tested on two benchmarks. The results show that some relevant relations, ignored by a similarity-based matching, can be found thanks to our approach.