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

TL;DR

The paper introduces OASIS, an importance sampling algorithm for entity resolution evaluation that significantly reduces labeling effort while maintaining accurate estimates of performance metrics.

Contribution

OASIS is a novel sampling and estimation method that adaptively focuses on informative samples using a Bayesian model, improving efficiency in ER evaluation.

Findings

Achieves up to 83% reduction in labeling effort

Provides consistent estimates of F-measure, precision, and recall

Demonstrates superior performance over existing sampling methods

Abstract

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.