Yet Another Matcher

Fabien Duchateau 1 Remi Coletta 2 Zohra Bellahsene 2 Renée Miller 3
2 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Discovering correspondences between schema elements is a crucial task for data integration. Most 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 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 (i) is able to generate the best matcher for a given scenario and (ii) easily integrates user preferences, namely recall and precision tradeoff.
Type de document :
Rapport
RR-09016, 2009
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Fabien Duchateau, Remi Coletta, Zohra Bellahsene, Renée Miller. Yet Another Matcher. RR-09016, 2009. 〈lirmm-00399025〉

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