How to Evaluate Entity Resolution Systems: An Entity-Centric Framework with Application to Inventor Name Disambiguation
Published in
arXiv.org(2024)External Links:
TL
TL;DR
This work proposes an entity-centric data labeling methodology that integrates with a unified framework for monitoring summary statistics, estimating key performance metrics such as cluster and pairwise precision and recall, and analyzing root causes for errors.
Abstract
Entity resolution (record linkage, microclustering) systems are notoriously difficult to evaluate. Looking for a needle in a haystack, traditional evaluation methods use sophisticated, application-specific sampling schemes to find matching pairs of records among an immense number of non-matches. We propose an alternative that facilitates the creation of representative, reusable benchmark data sets without necessitating complex sampling schemes. These benchmark data sets can then be used for model training and a variety of evaluation tasks. Specifically, we propose an entity-centric data labeling methodology that integrates with a unified framework for monitoring summary statistics, estimating key performance metrics such as cluster and pairwise precision and recall, and analyzing root causes for errors. We validate the framework in an application to inventor name disambiguation and through simulation studies. Software: https://github.com/OlivierBinette/er-evaluation/
References67 items
1
ER-Evaluation: End-to-End Evaluation of Entity Resolution Systems
2
PatentsView-Evaluation: Evaluation Datasets and Tools to Advance Research on Inventor Name Disambiguation
3
Estimating the Performance of Entity Resolution Algorithms: Lessons Learned Through PatentsView.org
4
The Importance of Being Ernest, Ekundayo, or Eswari: An Interpretable Machine Learning Approach to Name-Based Ethnicity Classification
5