Explaianable Entity Matching

Understanding if entries in a dataset refer to the same real-world entity (i.e., entity matching – EM) is a challenging task even for human experts.

State-of-the-art approaches based on Machine Learning (ML) and Deep Learning (DL) models are highly accurate but suffer from low interpretability. From the user’s perspective, these models act as oracles. This is a critical problem in many operational scenarios where traceability, scrutiny, and users’ confidence in the model are fundamental requirements as well as the model accuracy.

In this area we developed

• Landmark Explanation, a generic and extensible framework that extends the capabilities of a post-hoc perturbation-based explainer over the EM scenario. Landmark Explanation generates perturbations that take advantage of the particular schemas of the EM datasets, thus generating explanations more accurate and more interesting for the users than the ones generated by competing approaches.
  • Andrea Baraldi, Francesco Del Buono, Matteo Paganelli, Francesco Guerra: Landmark Explanation: An Explainer for Entity Matching Models. CIKM 2021: 4680-4684
  • Andrea Baraldi, Francesco Del Buono, Matteo Paganelli, Francesco Guerra: Using Landmarks for Explaining Entity Matching Models. EDBT 2021: 451-456