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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)

Paper ID
6091
Venue
SIGMOD
Year
2021
Pagerank
6.4336209e-05
Overall Rank
4,940 | 66.11%
DOI
10.1145/3448016.3452747

Incoming Non-self Citations Over Time

Authors

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}
}

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