2015/09/14 by Matt Barnes, Barnes, Matt
Computer Science · Decision Sciences · #Cloud Data Security Solutions #Data Quality and Management #Databases (cs.DB) #FOS: Computer and information sciences #Machine Learning (stat.ML) #Privacy-Preserving Technologies in Data
paper · pdf · doi:10.48550/arxiv.1509.04238
openalex publication_date 2015/09/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.