The Battleship Approach to the Low Resource Entity Matching Problem
Summary: Battleship-inspired active learning for low-resource entity matching; uses space-aware, distributed tuple-pair representations to gauge informativeness. Outperforms top active-learning baselines with fewer labels, approaching fully trained models. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Bar Genossar (Technion)
- 2. Avigdor Gal (Technion)
- 3. Roee Shraga (Worcester Polytechnic Institute)
BibTeX Citation
@inproceedings{genossar_sigmod23,
title = {{The Battleship Approach to the Low Resource Entity Matching Problem}},
author = {Genossar, Bar and Gal, Avigdor and Shraga, Roee},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3626711},
url = {https://dl.acm.org/doi/10.1145/3626711},
year = {2023}
}
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,992 | Progressive Entity Matching: A Design Space Exploration | 2025 | SIGMOD | 5.1815618e-05 |
| 10,203 | BEACON: Budget-Aware Entity Matching Across Domains | 2026 | SIGMOD | 5.093636e-05 |
| 10,334 | 3dSAGER: Geospatial Entity Resolution over 3D Objects | 2026 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 16 of 16 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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