Evaluating Entity Resolution Results

13 years 2 months ago

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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 conﬂict 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) inspired by the edit distance of strings, using cluster splits and merges as its basic operations. A signiﬁcant advantage of GMD is that the cost functions for splits and merges can be conﬁgured, enabling us to clearly understand the characteristics of a deﬁned GMD measure. Surprisingly, a state-of-the-art clustering measur...

David Menestrina, Steven Whang, Hector Garcia-Moli

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