CEO: Counterfactual Explanations for Ontologies

Tracking #: 3566-4780

This paper is currently under review
Matthieu Bellucci
Nicolas Delestre
Nicolas Malandain
Cecilia Zanni-Merk

Responsible editor: 
Guest Editors Interactive SW 2022

Submission type: 
Full Paper
Debugging and repairing Web Ontology Language (OWL) ontologies has been a key field of research since OWL became a W3C recommendation. One way to understand errors and fix them is done through explanations. These explanations are usually extracted from the reasoner and displayed to the ontology authors as is. In the meantime, there has been a recent call in the eXplainable AI (XAI) field to use expert knowledge in the form of knowledge graphs and ontologies. In this paper, a parallel between explanations for machine learning and for ontologies is drawn. This link enables the adaptation of XAI methods to explain ontologies and their entailments. Counterfactual explanations have been identified as a good candidate to solve the explainability problem in machine learning. The CEO (Counterfactual Explanations for Ontologies) method is thus proposed to explain inconsistent ontologies using counterfactual explanations. A preliminary user-study is conducted to ensure that using XAI methods for ontologies is relevant and worth pursuing.
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