A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching
Summary: Unifies active learning for Entity Matching into a benchmark framework to compose learning and selection strategies. On public EM data, active learning with fewer labels can match or beat supervised results; optimizations boost F1 ~9% and cut latency up to 10x. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Vamsi Meduri (Arizona State University)
- 2. Lucian Popa (IBM)
- 3. Prithviraj Sen (IBM)
- 4. Mohamed Sarwat (Arizona State University)
BibTeX Citation
@inproceedings{meduri_sigmod20,
title = {{A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching}},
author = {Meduri, Vamsi and Popa, Lucian and Sen, Prithviraj and Sarwat, Mohamed},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3380597},
url = {https://dl.acm.org/doi/10.1145/3318464.3380597},
year = {2020}
}
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