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Entity matching is a crucial and difficult task for data integration.
An effective solution strategy typically has to combine several
techniques and to find suitable settings for critical configuration
parameters such as similarity thresholds. Supervised (training-based)
approaches promise to reduce the manual work for
determining (learning) effective strategies for entity matching.
However, they critically depend on training data selection which
is a difficult problem that has so far mostly been addressed
manually by human experts. In this paper we propose a training-based
framework called STEM for entity matching and present
different generic methods for automatically selecting training data
to combine and configure several matching techniques. We
evaluate the proposed methods for different match tasks and
small- and medium-sized training sets.