In Search of an Entity Resolution OASIS: Optimal Asymptotic Sequential Importance Sampling
This addresses the problem of high labeling costs for researchers and practitioners in entity resolution, offering a significant improvement over existing methods.
The paper tackles the challenge of evaluating entity resolution (ER) systems by introducing the OASIS algorithm, which reduces labeling requirements by up to 83% while maintaining accurate estimates of F-measure, precision, and recall.
Entity resolution (ER) presents unique challenges for evaluation methodology. While crowdsourcing platforms acquire ground truth, sound approaches to sampling must drive labelling efforts. In ER, extreme class imbalance between matching and non-matching records can lead to enormous labelling requirements when seeking statistically consistent estimates for rigorous evaluation. This paper addresses this important challenge with the OASIS algorithm: a sampler and F-measure estimator for ER evaluation. OASIS draws samples from a (biased) instrumental distribution, chosen to ensure estimators with optimal asymptotic variance. As new labels are collected OASIS updates this instrumental distribution via a Bayesian latent variable model of the annotator oracle, to quickly focus on unlabelled items providing more information. We prove that resulting estimates of F-measure, precision, recall converge to the true population values. Thorough comparisons of sampling methods on a variety of ER datasets demonstrate significant labelling reductions of up to 83% without loss to estimate accuracy.