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Machine-learning-based entity resolution has been widely studied. However,some entity pairs may be mislabeled by machine learning models and existingstudies do not study the risk analysis problem -- predicting and interpretingwhich entity pairs are mislabeled. In this paper, we propose an interpretableand learnable framework for risk analysis, which aims to rank the labeled pairsbased on their risks of being mislabeled. We first describe how toautomatically generate interpretable risk features, and then present alearnable risk model and its training technique. Finally, we empiricallyevaluate the performance of the proposed approach on real data. Our extensiveexperiments have shown that the learning risk model can identify the mislabeledpairs with considerably higher accuracy than the existing alternatives.

Towards Interpretable and Learnable Risk Analysis for Entity Resolution