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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
- 6323
- Venue
- SIGMOD
- Year
- 2022
- Pagerank
- 5.0017341e-05
- Overall Rank
- 6,570 | 54.34%
- 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 |
| 4,211 |
Unicorn: A Unified Multi-tasking Model for Supporting Matching Tasks in Data Integration |
2023 |
SIGMOD |
6.3495931e-05 |
| 4,908 |
Combining Small Language Models and Large Language Models for Zero-Shot NL2SQL |
2024 |
VLDB |
5.835596e-05 |
| 8,401 |
DADER: Hands-Off Entity Resolution with Domain Adaptation |
2022 |
VLDB |
4.5176737e-05 |
| 8,828 |
HAIPipe: Combining Human-generated and Machine-generated Pipelines for Data Preparation |
2023 |
SIGMOD |
4.4364918e-05 |
| 8,913 |
PromptEM: Prompt-tuning for Low-resource Generalized Entity Matching |
2023 |
VLDB |
4.4229886e-05 |
| 9,073 |
VerifAI: Verified Generative AI |
2024 |
CIDR |
4.396857e-05 |
| 9,394 |
CEDA: Learned Cardinality Estimation with Domain Adaptation |
2023 |
VLDB |
4.3401448e-05 |
| 9,439 |
Rock: Cleaning Data by Embedding ML in Logic Rules |
2024 |
SIGMOD |
4.3389137e-05 |
| 10,603 |
Optimized Batch Prompting for Cost-effective LLMs |
2025 |
VLDB |
4.1905499e-05 |
| 10,690 |
AutoPrep: Natural Language Question-Aware Data Preparation with a Multi-Agent Framework |
2025 |
VLDB |
4.1905499e-05 |
| 11,050 |
Blocker and Matcher Can Mutually Benefit: A Co-Learning Framework for Low-Resource Entity Resolution |
2024 |
VLDB |
4.1905499e-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 |
| 219 |
Deep Entity Matching with Pre-Trained Language Models |
2021 |
VLDB |
0.00033354456 |
| 293 |
Deep Learning for Entity Matching: A Design Space Exploration |
2018 |
SIGMOD |
0.00028661817 |
| 700 |
Reasoning about Record Matching Rules |
2009 |
VLDB |
0.00017927576 |
| 740 |
Distributed Representations of Tuples for Entity Resolution |
2018 |
VLDB |
0.00017358024 |
| 1,821 |
Synthesizing Entity Matching Rules by Examples |
2018 |
VLDB |
0.00010406856 |
| 2,348 |
RPT: Relational Pre-trained Transformer Is Almost All You Need towards Democratizing Data Preparation |
2021 |
VLDB |
8.9903659e-05 |
| 3,324 |
iCrowd: An Adaptive Crowdsourcing Framework |
2015 |
SIGMOD |
7.2163115e-05 |
| 3,469 |
Deep Learning for Blocking in Entity Matching: A Design Space Exploration |
2021 |
VLDB |
7.0629476e-05 |
| 3,854 |
Generating Concise Entity Matching Rules |
2017 |
SIGMOD |
6.697423e-05 |
| 5,026 |
Adaptive Data Augmentation for Supervised Learning over Missing Data |
2021 |
VLDB |
5.7451454e-05 |
| 5,254 |
CDB: A Crowd-Powered Database System |
2018 |
VLDB |
5.5991922e-05 |
| 5,369 |
Cost-Effective Crowdsourced Entity Resolution: A Partial-Order Approach |
2016 |
SIGMOD |
5.5436995e-05 |
| 6,873 |
Cost-Effective Data Annotation using Game-Based Crowdsourcing |
2019 |
VLDB |
4.8963037e-05 |
| 11,796 |
CDB: Optimizing Queries with Crowd-Based Selections and Joins |
2017 |
SIGMOD |
4.1905499e-05 |
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