OmniMatch: Joinability Discovery in Data Products
Summary: OmniMatch: joinability discovery for curated data products that fuses multiple column-pair similarity measures with a self-supervised GNN exploiting graph neighborhood to boost recall. Automated negative-pair generation raises precision, yielding up to 14% F1/AUC gains without per-metric thresholds. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Christos Koutras (Delft University of Technology)
- 2. Jiani Zhang (Google)
- 3. Xiao Qin (Amazon)
- 4. Chuan Lei (Amazon)
- 5. Vasileios Ioannidis (Amazon)
- 6. Christos Faloutsos (Amazon; Carnegie Mellon University)
- 7. George Karypis (Amazon)
- 8. Asterios Katsifodimos (Amazon; Delft University of Technology)
BibTeX Citation
@article{koutras_vldb25,
title = {{OmniMatch: Joinability Discovery in Data Products}},
author = {Koutras, Christos and Zhang, Jiani and Qin, Xiao and Lei, Chuan and Ioannidis, Vasileios and Faloutsos, Christos and Karypis, George and Katsifodimos, Asterios},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {11},
pages = {4588--4601},
doi = {10.14778/3749646.3749715},
url = {https://doi.org/10.14778/3749646.3749715},
year = {2025}
}
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