# Estimating the Performance of Entity Resolution Algorithms: Lessons Learned Through PatentsView.org

arXiv: https://arxiv.org/abs/2210.01230 · HTML: https://mlsanity.com/paper/2210.01230/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 59 references that are in the map.
- First posted on arXiv 3 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [(Almost) All of Entity Resolution](https://mlsanity.com/paper/2008.04443/) (2020)
- [S2AND: A Benchmark and Evaluation System for Author Name Disambiguation](https://mlsanity.com/paper/2103.07534/) (2021)
- [Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction](https://mlsanity.com/paper/2205.05889/) (2022)
- [Optimal Transport-based Alignment of Learned Character Representations for String Similarity](https://mlsanity.com/paper/1907.10165/) (2019)
- [Scalable Hierarchical Clustering with Tree Grafting](https://mlsanity.com/paper/2001.00076/) (2019)
- [In Search of an Entity Resolution OASIS: Optimal Asymptotic Sequential Importance Sampling](https://mlsanity.com/paper/1703.00617/) (2017)
- [Scaling Bayesian Probabilistic Record Linkage with Post-Hoc Blocking: An Application to the California Great Registers](https://mlsanity.com/paper/1905.05337/) (2019)
- [A Practioner's Guide to Evaluating Entity Resolution Results](https://mlsanity.com/paper/1509.04238/) (2015)
- [Random Forest DBSCAN for USPTO Inventor Name Disambiguation](https://mlsanity.com/paper/1602.01792/) (2016)

## Built on by (most cited first, 3 of 3)

- [Improving the Validity and Practical Usefulness of AI/ML Evaluations Using an Estimands Framework](https://mlsanity.com/paper/2406.10366/) (2024)
- [PatentsView-Evaluation: Evaluation Datasets and Tools to Advance Research on Inventor Name Disambiguation](https://mlsanity.com/paper/2301.03591/) (2023)
- [How to Evaluate Entity Resolution Systems: An Entity-Centric Framework with Application to Inventor Name Disambiguation](https://mlsanity.com/paper/2404.05622/) (2024)

## Abstract (arXiv)

This paper introduces a novel evaluation methodology for entity resolution algorithms. It is motivated by PatentsView.org, a U.S. Patents and Trademarks Office patent data exploration tool that disambiguates patent inventors using an entity resolution algorithm. We provide a data collection methodology and tailored performance estimators that account for sampling biases. Our approach is simple, practical and principled -- key characteristics that allow us to paint the first representative picture of PatentsView's disambiguation performance. This approach is used to inform PatentsView's users of the reliability of the data and to allow the comparison of competing disambiguation algorithms.