  Matching is a task at the heart of any data integration process, aimed at
identifying correspondences among data elements. Matching problems were
traditionally solved in a semi-automatic manner, with correspondences being
generated by matching algorithms and outcomes subsequently validated by human
experts. Human-in-the-loop data integration has been recently challenged by the
introduction of big data and recent studies have analyzed obstacles to
effective human matching and validation. In this work we characterize human
matching experts, those humans whose proposed correspondences can mostly be
trusted to be valid. We provide a novel framework for characterizing matching
experts that, accompanied with a novel set of features, can be used to identify
reliable and valuable human experts. We demonstrate the usefulness of our
approach using an extensive empirical evaluation. In particular, we show that
our approach can improve matching results by filtering out inexpert matchers.
