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
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Learning to match ontologies on the Semantic Web
The VLDB Journal — The International Journal on Very Large Data Bases
Schema and ontology matching with COMA++
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
eTuner: tuning schema matching software using synthetic scenarios
The VLDB Journal — The International Journal on Very Large Data Bases
Ontology Matching
Proceedings of the 18th ACM conference on Information and knowledge management
Ontology alignment evaluation initiative: six years of experience
Journal on data semantics XV
Ontology alignment using artificial neural network for large-scale ontologies
International Journal of Metadata, Semantics and Ontologies
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Discovering correspondences between schema elements is a crucial task for data integration. Most schema matching tools are semi-automatic, e.g. an expert must tune some parameters (thresholds, weights, etc.). They mainly use several methods to combine and aggregate similarity measures. However, their quality results often decrease when one requires to integrate a new similarity measure or when matching particular domain schemas. This paper describes YAM (Yet Another Matcher), which is a schema matcher factory. Indeed, it enables the generation of a dedicated matcher for a given schema matching scenario, according to user inputs. Our approach is based on machine learning since schema matchers can be seen as classifiers. Several bunches of experiments run against matchers generated by YAM and traditional matching tools show how our approach is able to generate the best matcher for a given scenario.