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

arXiv: https://arxiv.org/abs/1703.00617 · HTML: https://mlsanity.com/paper/1703.00617/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2017.

## Built on by (most cited first, 4 of 4)

- [Calibrated Decisions at Scale: Converting Police Crash Narratives into Probabilistic Crash Variables with a System One Model (Jev)](https://mlsanity.com/paper/2609.24052/) (2026)
- [Estimating the Performance of Entity Resolution Algorithms: Lessons Learned Through PatentsView.org](https://mlsanity.com/paper/2210.01230/) (2022)
- [How to Evaluate Entity Resolution Systems: An Entity-Centric Framework with Application to Inventor Name Disambiguation](https://mlsanity.com/paper/2404.05622/) (2024)
- [Label-Efficient Monitoring of Classification Models via Stratified Importance Sampling](https://mlsanity.com/paper/2601.22326/) (2026)

## Abstract (arXiv)

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.