PoWareMatch: a Quality-aware Deep Learning Approach to Improve Human Schema Matching

TL;DR

PoWareMatch is a deep learning-based system that enhances human schema matching by calibrating and filtering decisions, leading to higher quality data integration matches and outperforming existing algorithms.

Contribution

This work introduces PoWareMatch, a novel approach combining human judgment and deep learning to improve schema matching quality and reliability.

Findings

PoWareMatch accurately predicts the benefit of adding correspondences.

It generates higher quality matches than existing algorithms.

Empirical validation with over 200 human matchers supports its effectiveness.

Abstract

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.