Automatic entity labeling through explanation techniques

Entity resolution (ER) aims at matching records that refer to the same real-world entity, e.g., the same product sold by different websites. Recent solutions to this problem have reached unprecedented accuracy. Nonetheless, due to intrinsic limitations of automatic testing methods, it is known among researchers and practitioners that a significant manual effort is still required in production environments for verification and cleaning of ER results. In order to facilitate such activity, we are developing the E2L methodology (Entity to Labels) for automatic computation of human-readable labels of identified entities. Given a selection of entities for which the user wants to compute labels, E2L first extracts relevant features by training a classifier on the ER results, then it leverages the notion of black-box model explanation to select the most important terms for the classifier, and finally it uses those terms to compute labels. In this paper we report our first experiences with E2L. Preliminary results on a real-world application scenario show that E2L labels can provide an accurate description of entities and a natural way for humans to assess the trustworthiness of ER results at a glance.