How to Evaluate Entity Resolution Systems: An Entity-Centric Framework with Application to Inventor Name Disambiguation
This addresses the problem of evaluating entity resolution systems for researchers and practitioners, offering a more efficient and standardized approach, though it is incremental as it builds on existing evaluation challenges.
The paper tackles the difficulty of evaluating entity resolution systems by proposing an entity-centric framework that facilitates the creation of representative, reusable benchmark datasets without complex sampling schemes, validated through an application to inventor name disambiguation and simulation studies.
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/