DADER: Hands-Off Entity Resolution with Domain Adaptation
Summary: Hands-off deep ER via domain adaptation: DADER trains on labeled source ER data to enable zero- or few-label targets. Source-pair selection, six domain-adaptation strategies for alignment, and an open-source Python library with optional user labeling. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jianhong Tu (Renmin University of China)
- 2. Xiaoyue Han (Renmin University of China)
- 3. Ju Fan (Renmin University of China)
- 4. Nan Tang (Qatar Computing Research Institute)
- 5. Chengliang Chai (Tsinghua University)
- 6. Guoliang Li (Tsinghua University)
- 7. Xiaoyong Du (Renmin University of China)
BibTeX Citation
@article{tu_vldb22,
title = {{DADER: Hands-Off Entity Resolution with Domain Adaptation}},
author = {Tu, Jianhong and Han, Xiaoyue and Fan, Ju and Tang, Nan and Chai, Chengliang and Li, Guoliang and Du, Xiaoyong},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {12},
pages = {3666--3669},
doi = {10.14778/3554821.3554870},
url = {https://doi.org/10.14778/3554821.3554870},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,177 | HAIPipe: Combining Human-generated and Machine-generated Pipelines for Data Preparation | 2023 | SIGMOD | 5.4730821e-05 |
| 9,647 | Rock: Cleaning Data by Embedding ML in Logic Rules | 2024 | SIGMOD | 5.2430158e-05 |
| 11,407 | When Automatic Filtering Comes to the Rescue: Pre-Computing Company Competitor Pairs in Owler | 2023 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 141 | Deep Entity Matching with Pre-Trained Language Models | 2021 | VLDB | 0.0002964847 |
| 176 | Deep Learning for Entity Matching: A Design Space Exploration | 2018 | SIGMOD | 0.00027191081 |
| 489 | Distributed Representations of Tuples for Entity Resolution | 2018 | VLDB | 0.0001761456 |
| 1,465 | Synthesizing Entity Matching Rules by Examples | 2018 | VLDB | 0.00010689571 |
| 5,730 | Domain Adaptation for Deep Entity Resolution | 2022 | SIGMOD | 6.1071585e-05 |
| 6,041 | Demonstration of Panda: A Weakly Supervised Entity Matching System | 2021 | VLDB | 5.9976574e-05 |
| 6,908 | Cost-Effective Data Annotation using Game-Based Crowdsourcing | 2019 | VLDB | 5.7415834e-05 |
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