Ground Truth Inference for Weakly Supervised Entity Matching
Summary: Weak supervision for entity matching via labeling functions; simple labeling model outperforms prior methods. EM transitivity; exact solutions when possible, ML approximations otherwise; DeepMatcher trained on weak labels rivals fully supervised models. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Renzhi Wu (Georgia Institute of Technology)
- 2. Alexander Bendeck (Georgia Institute of Technology)
- 3. Xu Chu (Georgia Institute of Technology)
- 4. Yeye He (Microsoft)
BibTeX Citation
@inproceedings{wu_sigmod23,
title = {{Ground Truth Inference for Weakly Supervised Entity Matching}},
author = {Wu, Renzhi and Bendeck, Alexander and Chu, Xu and He, Yeye},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3588712},
url = {https://dl.acm.org/doi/10.1145/3588712},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,242 | Blocker and Matcher Can Mutually Benefit: A Co-Learning Framework for Low-Resource Entity Resolution | 2024 | VLDB | 5.0418764e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 19 of 19 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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| 4 | 9,823 | The Battleship Approach to the Low Resource Entity Matching Problem | 2023 | SIGMOD |
| 5 | 10,242 | Blocker and Matcher Can Mutually Benefit: A Co-Learning Framework for Low-Resource Entity Resolution | 2024 | VLDB |
| 6 | 2,271 | ZeroER: Entity Resolution using Zero Labeled Examples | 2020 | SIGMOD |
| 7 | 2,520 | Deep Learning for Blocking in Entity Matching: A Design Space Exploration | 2021 | VLDB |
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| 10 | 6,185 | Demonstration of Panda: A Weakly Supervised Entity Matching System | 2021 | VLDB |