Evaluating entity resolution results

Abstract

Entity Resolution (ER) is the process of identifying groups of records that refer to the same real-world entity. Various measures (e.g., pairwise F1, cluster F1) have been used for evaluating ER results. However, ER measures tend to be chosen in an ad-hoc fashion without careful thought as to what defines a good result for the specific application at hand. In this paper, our contributions are twofold. First, we conduct an analysis on existing ER measures, showing that they can often con ict with each other by ranking the results of ER algorithms differently. Second, we explore a new distance measure for ER (called “generalized merge distance” or GMD)