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

arXiv: https://arxiv.org/abs/2109.07321 · HTML: https://mlsanity.com/paper/2109.07321/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 64 references that are in the map.
- First posted on arXiv 15 Sep 2021.

## Builds on (most cited first, 2 of 2)

- [DeepER -- Deep Entity Resolution](https://mlsanity.com/paper/1710.00597/) (2017)
- [Reducing Uncertainty of Schema Matching via Crowdsourcing with Accuracy Rates](https://mlsanity.com/paper/1809.04017/) (2018)

## Built on by (most cited first, 4 of 4)

- [HumanAL: Calibrating Human Matching Beyond a Single Task](https://mlsanity.com/paper/2205.03209/) (2022)
- [Humans, Machine Learning, and Language Models in Union: A Cognitive Study on Table Unionability](https://mlsanity.com/paper/2506.12990/) (2025)
- [RACT: Retrieval Augmented Column-Table Learning and Prediction for Multi-Table Schema Matching](https://mlsanity.com/paper/2606.07843/) (2026)
- [Human's Role in-the-Loop](https://mlsanity.com/paper/2204.14192/) (2022)

## Abstract (arXiv)

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.