Deep Learning for Entity Matching: A Design Space Exploration
Summary: Explores deep learning for entity matching, defines a DL design space (SIF, RNN, Attention, Hybrid), and maps NLP-style methods to EM. Empirically, DL lags on structured EM vs Magellan but excels on textual and dirty EM, guiding DL use for noisy data and outlining directions. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Sidharth Mudgal
- 2. Han Li
- 3. Theodoros Rekatsinas
- 4. AnHai Doan
- 5. Youngchoon Park
- 6. Ganesh Krishnan
- 7. Rohit Deep
- 8. Esteban Arcaute
- 9. Vijay Raghavendra
Incoming Citations (Sorted by Pagerank)
Showing 35 of 85 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 254 | Snorkel: Rapid Training Data Creation with Weak Supervision | 2018 | VLDB | 0.00030540555 |
| 263 | CrowdER: Crowdsourcing Entity Resolution | 2012 | VLDB | 0.00029862413 |
| 319 | Evaluation of entity resolution approaches on real-world match problems | 2010 | VLDB | 0.00027781866 |
| 489 | Data Curation at Scale: The Data Tamer System | 2013 | CIDR | 0.00022030728 |
| 643 | Corleone: Hands-Off Crowdsourcing for Entity Matching | 2014 | SIGMOD | 0.00018754451 |
| 702 | Reasoning about Record Matching Rules | 2009 | VLDB | 0.00017918203 |
| 712 | Magellan: Toward Building Entity Matching Management Systems | 2016 | VLDB | 0.00017732426 |
| 814 | Entity Resolution: Theory, Practice & Open Challenges | 2012 | VLDB | 0.00016370594 |
| 2,514 | Comparative Analysis of Approximate Blocking Techniques for Entity Resolution | 2016 | VLDB | 8.6139012e-05 |
| 3,303 | Fonduer: Knowledge Base Construction from Richly Formatted Data | 2018 | SIGMOD | 7.2487486e-05 |
| 3,861 | Generating Concise Entity Matching Rules | 2017 | SIGMOD | 6.6878164e-05 |
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