Reconciling schemas of disparate data sources: a machine-learning approach
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Machine Learning
Database Schema Matching Using Machine Learning with Feature Selection
CAiSE '02 Proceedings of the 14th International Conference on Advanced Information Systems Engineering
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
COMA: a system for flexible combination of schema matching approaches
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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Proceedings of the 28th international conference on Software engineering
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Information Systems
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Proceedings of the 2010 ACM Symposium on Applied Computing
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Journal of Computer Science and Technology
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ACM SIGAPP Applied Computing Review
LCS: a linguistic combination system for ontology matching
KSEM'06 Proceedings of the First international conference on Knowledge Science, Engineering and Management
Combining human and computation intelligence: the case of data interlinking tools
International Journal of Metadata, Semantics and Ontologies
Non-binary evaluation for schema matching
ER'12 Proceedings of the 31st international conference on Conceptual Modeling
Combining human and computation intelligence: the case of data interlinking tools
International Journal of Metadata, Semantics and Ontologies
Schema matching prediction with applications to data source discovery and dynamic ensembling
The VLDB Journal — The International Journal on Very Large Data Bases
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Schema matching is a key operation in data engineering. Combining multiple matching strategies is a very promising technique for schema matching. To overcome the limitations of existing combination systems and to achieve better performances, in this paper the CMC system is proposed, which combines multiple matchers based on credibility prediction. We first predict the accuracy of each matcher on the current matching task, and accordingly calculate each matcher's credibility. These credibilities are then used as weights in aggregating the matching results of different matchers into a combined one. Our experiments on real world schemas validate the merits of our system.