Evaluation measure for group-based record linkage

The robustness of record linkage evaluation measures is of high importance since linkage techniques are assessed based on these. However, minimal research has been conducted to evaluate the suitability of existing evaluation measures in the context of linking groups of records. Linkage quality is generally evaluated based on traditional measures such as precision and recall. As we show, these traditional evaluation measures are not suitable for evaluating groups of linked records because they evaluate the quality of individual record pairs rather than the quality of records grouped into clusters.

We set out to highlight the shortcomings of traditional evaluation measures and then propose a novel method to evaluate clustering quality in the context of group-based record linkage.

The proposed linkage evaluation method assesses how well individual records have been allocated into predicted groups/clusters with respect to ground-truth data. We first identify the best representative predicted cluster for each ground-truth cluster and, based on the resulting mapping, each record in a ground-truth cluster is assigned to one of seven categories. These categories reflect how well the linkage technique assigned records into groups.

We empirically evaluated our proposed method using real-world data and showed that it better reflects the quality of clusters generated by three group-based record linkage techniques. We also showed that traditional measures such as precision and recall can produce ambiguous results whereas our method does not.

The proposed evaluation method provides unambiguous results regarding the assessed group-based record linkage approaches. The method comprises of seven categories which reflect how each record was predicted, providing more detailed information about the quality of the linkage result. This will help to make better-informed decisions about which linkage technique is best suited for a given linkage application.