Deep Learning for Blocking in Entity Matching: A Design Space Exploration
Summary: DeepBlocker systematically explores a broad design space for deep-learning entity-matching blockers, including sequence models, transformers, and self-supervision without labeled data. Its best variants outperform prior DL/non-DL blockers on dirty and textual data, while hybrid DL–traditional blocking improves further. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Saravanan Thirumuruganathan (Hamad Bin Khalifa University; Qatar Computing Research Institute)
- 2. Han Li (Amazon)
- 3. Nan Tang (Hamad Bin Khalifa University; Qatar Computing Research Institute)
- 4. Mourad Ouzzani (Hamad Bin Khalifa University; Qatar Computing Research Institute)
- 5. Yash Govind (Informatica)
- 6. Derek Paulsen (Informatica; University of Wisconsin)
- 7. Glenn Fung (American Family Insurance)
- 8. AnHai Doan (Informatica; University of Wisconsin)
BibTeX Citation
@article{thirumuruganathan_vldb21,
title = {{Deep Learning for Blocking in Entity Matching: A Design Space Exploration}},
author = {Thirumuruganathan, Saravanan and Li, Han and Tang, Nan and Ouzzani, Mourad and Govind, Yash and Paulsen, Derek and Fung, Glenn and Doan, AnHai},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {11},
pages = {2459--2472},
doi = {10.14778/3476249.3476294},
url = {https://doi.org/10.14778/3476249.3476294},
year = {2021}
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