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