A Practioner's Guide to Evaluating Entity Resolution Results
This is an incremental survey aimed at practitioners in data integration and cleaning to help them evaluate entity resolution systems effectively.
The paper tackles the problem of evaluating entity resolution results by surveying existing metrics, highlighting that these metrics often conflict, and provides practitioners with basic knowledge to assess and improve performance before deployment.
Entity resolution (ER) is the task of identifying records belonging to the same entity (e.g. individual, group) across one or multiple databases. Ironically, it has multiple names: deduplication and record linkage, among others. In this paper we survey metrics used to evaluate ER results in order to iteratively improve performance and guarantee sufficient quality prior to deployment. Some of these metrics are borrowed from multi-class classification and clustering domains, though some key differences exist differentiating entity resolution from general clustering. Menestrina et al. empirically showed rankings from these metrics often conflict with each other, thus our primary motivation for studying them. This paper provides practitioners the basic knowledge to begin evaluating their entity resolution results.