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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)

Paper ID
5810
Venue
SIGMOD
Year
2020
Pagerank
8.1513883e-05
Overall Rank
2,767 | 80.76%
DOI
10.1145/3318464.3380597

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