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In Search of an Entity Resolution OASIS: Optimal Asymptotic Sequential Importance Sampling

Summary: Proposes OASIS, an asymptotic sequential IS method for evaluating entity resolution. Biased sampling with a Bayesian latent-variable annotator model targets informative unlabeled items, preserving convergence of F-measure, precision/recall; achieves 83% labeling reduction. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11417
Venue
VLDB
Year
2017
Pagerank
5.2847867e-05
Overall Rank
5,896 | 58.99%
DOI
-

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Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
6,689 Efficient Knowledge Graph Accuracy Evaluation 2019 VLDB 4.9623586e-05
11,029 Efficient and Reliable Estimation of Knowledge Graph Accuracy 2024 VLDB 4.1945683e-05
11,342 FILA: Online Auditing of Machine Learning Model Accuracy under Finite Labelling Budget 2022 SIGMOD 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
319 Evaluation of entity resolution approaches on real-world match problems 2010 VLDB 0.00027781866
3,118 Scaling Up Crowd-Sourcing to Very Large Datasets: A Case for Active Learning 2015 VLDB 7.5379338e-05
3,177 Evaluating Entity Resolution Results 2010 VLDB 7.4367331e-05
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