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Matching is a task at the heart of any data integration process, aimed atidentifying correspondences among data elements. Matching problems weretraditionally solved in a semi-automatic manner, with correspondences beinggenerated by matching algorithms and outcomes subsequently validated by humanexperts. Human-in-the-loop data integration has been recently challenged by theintroduction of big data and recent studies have analyzed obstacles toeffective human matching and validation. In this work we characterize humanmatching experts, those humans whose proposed correspondences can mostly betrusted to be valid. We provide a novel framework for characterizing matchingexperts that, accompanied with a novel set of features, can be used to identifyreliable and valuable human experts. We demonstrate the usefulness of ourapproach using an extensive empirical evaluation. In particular, we show thatour approach can improve matching results by filtering out inexpert matchers.

Learning to Characterize Matching Experts