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 eort is needed to obtain a correct set of attribute correspondences.