ZeroER: Entity Resolution using Zero Labeled Examples
Summary: ZeroER uses zero-labeled data for ER with a Gaussian Mixture Model separating match vs. unmatch. It adds adaptive regularization and a transitivity-informed generative model, yielding strong unsupervised results close to supervised on five ER benchmarks. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Renzhi Wu (Georgia Institute of Technology)
- 2. Sanya Chaba (Georgia Institute of Technology)
- 3. Saurabh Sawlani (Georgia Institute of Technology)
- 4. Xu Chu (Georgia Institute of Technology)
- 5. Saravanan Thirumuruganathan (Hamad Bin Khalifa University; Qatar Computing Research Institute)
BibTeX Citation
@inproceedings{wu_sigmod20,
title = {{ZeroER: Entity Resolution using Zero Labeled Examples}},
author = {Wu, Renzhi and Chaba, Sanya and Sawlani, Saurabh and Chu, Xu and Thirumuruganathan, Saravanan},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3389743},
url = {https://dl.acm.org/doi/10.1145/3318464.3389743},
year = {2020}
}Incoming Citations (Sorted by Pagerank)
Showing 25 of 25 citing papers.
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 15 of 15 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 10,049 | Towards Interpretable and Learnable Risk Analysis for Entity Resolution | 2020 | SIGMOD |
| 2 | 10,318 | In-context Clustering-based Entity Resolution with Large Language Models: A Design Space Exploration | 2026 | SIGMOD |
| 3 | 248 | Evaluation of entity resolution approaches on real-world match problems | 2010 | VLDB |
| 4 | 9,818 | Hierarchical Entity Resolution using an Oracle | 2022 | SIGMOD |
| 5 | 11,255 | Blocker and Matcher Can Mutually Benefit: A Co-Learning Framework for Low-Resource Entity Resolution | 2024 | VLDB |
| 6 | 9,560 | Ground Truth Inference for Weakly Supervised Entity Matching | 2023 | SIGMOD |
| 7 | 5,730 | Domain Adaptation for Deep Entity Resolution | 2022 | SIGMOD |
| 8 | 11,226 | ZeroEA: A Zero-Training Entity Alignment Framework via Pre-Trained Language Model | 2024 | VLDB |
| 9 | 8,592 | Robust Entity Resolution using Random Graphs | 2018 | SIGMOD |
| 10 | 5,830 | Exploiting Context Analysis for Combining Multiple Entity Resolution Systems | 2009 | SIGMOD |