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Domain Adaptation for Deep Entity Resolution
Summary: Domain Adaptation for Deep Entity Resolution transfers DL-ER models from labeled sources to unlabeled or sparsely labeled targets. Three-module space—Feature Extractor, Matcher, Feature Aligner—and an empirical study guiding DA choices for ER.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 6322
- Venue
- SIGMOD
- Year
- 2022
- Pagerank
- 5.0065379e-05
- Overall Rank
- 6,569 | 54.31%
- DOI
-
10.1145/3514221.3517870
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 11 of 11 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 4,212 |
Unicorn: A Unified Multi-tasking Model for Supporting Matching Tasks in Data Integration |
2023 |
SIGMOD |
6.3555142e-05 |
| 4,908 |
Combining Small Language Models and Large Language Models for Zero-Shot NL2SQL |
2024 |
VLDB |
5.8339245e-05 |
| 8,406 |
DADER: Hands-Off Entity Resolution with Domain Adaptation |
2022 |
VLDB |
4.5220083e-05 |
| 8,828 |
HAIPipe: Combining Human-generated and Machine-generated Pipelines for Data Preparation |
2023 |
SIGMOD |
4.4407488e-05 |
| 8,911 |
PromptEM: Prompt-tuning for Low-resource Generalized Entity Matching |
2023 |
VLDB |
4.427232e-05 |
| 9,077 |
VerifAI: Verified Generative AI |
2024 |
CIDR |
4.4010762e-05 |
| 9,388 |
CEDA: Learned Cardinality Estimation with Domain Adaptation |
2023 |
VLDB |
4.3443083e-05 |
| 9,434 |
Rock: Cleaning Data by Embedding ML in Logic Rules |
2024 |
SIGMOD |
4.3430376e-05 |
| 10,595 |
Optimized Batch Prompting for Cost-effective LLMs |
2025 |
VLDB |
4.1945683e-05 |
| 10,682 |
AutoPrep: Natural Language Question-Aware Data Preparation with a Multi-Agent Framework |
2025 |
VLDB |
4.1945683e-05 |
| 11,047 |
Blocker and Matcher Can Mutually Benefit: A Co-Learning Framework for Low-Resource Entity Resolution |
2024 |
VLDB |
4.1945683e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 221 |
Deep Entity Matching with Pre-Trained Language Models |
2021 |
VLDB |
0.00033121824 |
| 300 |
Deep Learning for Entity Matching: A Design Space Exploration |
2018 |
SIGMOD |
0.00028441466 |
| 702 |
Reasoning about Record Matching Rules |
2009 |
VLDB |
0.00017918203 |
| 754 |
Distributed Representations of Tuples for Entity Resolution |
2018 |
VLDB |
0.00017117211 |
| 1,831 |
Synthesizing Entity Matching Rules by Examples |
2018 |
VLDB |
0.00010384082 |
| 2,349 |
RPT: Relational Pre-trained Transformer Is Almost All You Need towards Democratizing Data Preparation |
2021 |
VLDB |
8.9876423e-05 |
| 3,322 |
iCrowd: An Adaptive Crowdsourcing Framework |
2015 |
SIGMOD |
7.2230626e-05 |
| 3,640 |
Deep Learning for Blocking in Entity Matching: A Design Space Exploration |
2021 |
VLDB |
6.8891671e-05 |
| 3,861 |
Generating Concise Entity Matching Rules |
2017 |
SIGMOD |
6.6878164e-05 |
| 5,028 |
Adaptive Data Augmentation for Supervised Learning over Missing Data |
2021 |
VLDB |
5.7506746e-05 |
| 5,279 |
CDB: A Crowd-Powered Database System |
2018 |
VLDB |
5.5902418e-05 |
| 5,362 |
Cost-Effective Crowdsourced Entity Resolution: A Partial-Order Approach |
2016 |
SIGMOD |
5.5473503e-05 |
| 6,868 |
Cost-Effective Data Annotation using Game-Based Crowdsourcing |
2019 |
VLDB |
4.9010083e-05 |
| 11,788 |
CDB: Optimizing Queries with Crowd-Based Selections and Joins |
2017 |
SIGMOD |
4.1945683e-05 |
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