Entity Resolution (ER) is the task of finding and merging entities within a single data source or across several data sources that represent the same real world entity. Evaluating ER results is a very important procedure used to ensure how accurate and correct an ER algorithm is. There have been several measures proposed and used for ER evaluation (e.g., pairwise F1, B3 F1, CEAF). With so many new and existing evaluation measures, there is a need to survey, characterize, and evaluate these measures. In this paper, we explore these ER evaluation measures. First, we provide a general definition to the ER problem. Next, we define many of the widely used measures, including a discussion of the different advantages and disadvantages of each measure. We also discuss trends in the use of the ER evaluation measures based across multiple domains. Finally, we provide examples of ER predictions and compute several of the measures we discuss to highlights differences and overlaps in the measures. In doing so, we hope to provide practitioners with a practical guide for understanding ER evaluation.
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