  Schema matching is a core task of any data integration process. Being
investigated in the fields of databases, AI, Semantic Web and data mining for
many years, the main challenge remains the ability to generate quality matches
among data concepts (e.g., database attributes). In this work, we examine a
novel angle on the behavior of humans as matchers, studying match creation as a
process. We analyze the dynamics of common evaluation measures (precision,
recall, and f-measure), with respect to this angle and highlight the need for
unbiased matching to support this analysis. Unbiased matching, a newly defined
concept that describes the common assumption that human decisions represent
reliable assessments of schemata correspondences, is, however, not an inherent
property of human matchers. In what follows, we design PoWareMatch that makes
use of a deep learning mechanism to calibrate and filter human matching
decisions adhering the quality of a match, which are then combined with
algorithmic matching to generate better match results. We provide an empirical
evidence, established based on an experiment with more than 200 human matchers
over common benchmarks, that PoWareMatch predicts well the benefit of extending
the match with an additional correspondence and generates high quality matches.
In addition, PoWareMatch outperforms state-of-the-art matching algorithms.
