On Leveraging Crowdsourcing Techniques for Schema Matching Networks

As the number of publicly-available datasets are likely to grow, the demand of establishing the links between these datasets is also getting higher and higher. For creating such links we need to match their schemas. Moreover, for using these datasets in meaningful ways, one often needs to match not only two, but several schemas. This matching process establishes a (potentially large) set of attribute correspondences between multiple schemas that constitute a schema matching network. Various commercial and academic schema matching tools have been developed to support this task. However, as the matching is inherently uncertain, the heuristic techniques adopted by these tools give rise to results that are not completely correct. Thus, in practice, a post-matching human expert effort is needed to obtain a correct set of attribute correspondences. Addressing this problem, our paper demonstrates how to leverage crowdsourcing techniques to validate the generated correspondences. We design validation questions with contextual information that can effectively guide the crowd workers. We analyze how to reduce overall human effort needed for this validation task. Through theoretical and empirical results, we show that by harnessing natural constraints defined on top of the schema matching network, one can significantly reduce the necessary human work.