LEMON: Explainable Entity Matching. (arXiv:2110.00516v2 [cs.DB] UPDATED)

State-of-the-art entity matching (EM) methods are hard to interpret, and
there is significant value in bringing explainable AI to EM. Unfortunately,
most popular explainability methods do not work well out of the box for EM and
need adaptation. In this paper, we identify three challenges of applying local
post hoc feature attribution methods to entity matching: cross-record
interaction effects, non-match explanations, and variation in sensitivity. We
propose our novel model-agnostic and schema-flexible method LEMON that
addresses all three challenges by …

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