The state of the art approaches for performing Entity Matching (EM) rely on machine & deep learning models for inferring pairs of matching / non-matching entities. Although the experimental evaluations demonstrate that these approaches are effective, their adoption in real scenarios is limited by the fact that they are difficult to interpret. Explainable AI systems have been recently proposed for complementing deep learning approaches. Their application to the scenario offered by EM is still new and requires to address the specificity of this task, characterized by particular dataset schemas, describing a pair of entities, and imbalanced classes. This paper introduces 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.

Using Landmarks for Explaining Entity Matching Models / Baraldi, Andrea; DEL BUONO, Francesco; Paganelli, Matteo; Guerra, Francesco. - 2021-March:(2021), pp. 451-456. ((Intervento presentato al convegno EDBT tenutosi a Nicosia nel 23-26 March 2021 [10.5441/002/edbt.2021.50].

Using Landmarks for Explaining Entity Matching Models

Andrea Baraldi;Francesco Del Buono;Matteo Paganelli;Francesco Guerra
2021

Abstract

The state of the art approaches for performing Entity Matching (EM) rely on machine & deep learning models for inferring pairs of matching / non-matching entities. Although the experimental evaluations demonstrate that these approaches are effective, their adoption in real scenarios is limited by the fact that they are difficult to interpret. Explainable AI systems have been recently proposed for complementing deep learning approaches. Their application to the scenario offered by EM is still new and requires to address the specificity of this task, characterized by particular dataset schemas, describing a pair of entities, and imbalanced classes. This paper introduces 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.
EDBT
Nicosia
23-26 March 2021
2021-March
451
456
Baraldi, Andrea; DEL BUONO, Francesco; Paganelli, Matteo; Guerra, Francesco
Using Landmarks for Explaining Entity Matching Models / Baraldi, Andrea; DEL BUONO, Francesco; Paganelli, Matteo; Guerra, Francesco. - 2021-March:(2021), pp. 451-456. ((Intervento presentato al convegno EDBT tenutosi a Nicosia nel 23-26 March 2021 [10.5441/002/edbt.2021.50].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1247078
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