BEER: Blocking for Effective Entity Resolution
Summary: BEER proposes progressive blocking for ER, using a feedback loop from ER output to refine candidate pruning. End-to-end, data-driven BEER provides visualization and explanations to compare blocking choices across cluster sizes, with no manual tuning. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Sainyam Galhotra (University of Massachusetts Amherst)
- 2. Donatella Firmani (Roma Tre University)
- 3. Barna Saha (University of California Berkeley)
- 4. Divesh Srivastava (AT&T)
BibTeX Citation
@inproceedings{galhotra_sigmod21,
title = {{BEER: Blocking for Effective Entity Resolution}},
author = {Galhotra, Sainyam and Firmani, Donatella and Saha, Barna and Srivastava, Divesh},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3452747},
url = {https://dl.acm.org/doi/10.1145/3448016.3452747},
year = {2021}
}
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,475 | Deep Learning for Blocking in Entity Matching: A Design Space Exploration | 2021 | VLDB | 8.5277654e-05 |
| 7,232 | Saga: A Scalable Framework for Optimizing Data Cleaning Pipelines for Machine Learning Applications | 2023 | SIGMOD | 5.6659017e-05 |
| 9,992 | Progressive Entity Matching: A Design Space Exploration | 2025 | SIGMOD | 5.1815618e-05 |
| 10,878 | Evaluating Methods for Efficient Entity Count Estimation | 2025 | VLDB | 5.093636e-05 |
| 11,571 | Generalized Supervised Meta-blocking | 2022 | VLDB | 5.093636e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 15 of 15 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 489 | Distributed Representations of Tuples for Entity Resolution | 2018 | VLDB |
| 2 | 4,938 | Supervised Meta-blocking | 2014 | VLDB |
| 3 | 2,475 | Deep Learning for Blocking in Entity Matching: A Design Space Exploration | 2021 | VLDB |
| 4 | 11,255 | Blocker and Matcher Can Mutually Benefit: A Co-Learning Framework for Low-Resource Entity Resolution | 2024 | VLDB |
| 5 | 3,665 | BLAST: a Loosely Schema-aware Meta-blocking Approach for Entity Resolution | 2016 | VLDB |
| 6 | 11,571 | Generalized Supervised Meta-blocking | 2022 | VLDB |
| 7 | 7,743 | Entity Resolution On-Demand | 2022 | VLDB |
| 8 | 2,120 | Comparative Analysis of Approximate Blocking Techniques for Entity Resolution | 2016 | VLDB |
| 9 | 9,611 | BrewER: Entity Resolution On-Demand | 2023 | VLDB |
| 10 | 1,293 | Entity Resolution with Iterative Blocking | 2009 | SIGMOD |