La recherche en Intelligence Artificielle Explicable (XAI) a souligné la nécessité de créer des modèles basés sur les connaissances du domaine pour qu’ils soient explicables du point de vue de leurs utilisateurs. Une part importante des travaux actuels se concentre sur la conception de modèles d’IA qui intègrent la sémantique et le mode de raisonnement des experts du domaine. S’inspirant des Modèles Basés sur les Concepts et des approches Neuro-Symboliques, nous proposons une architecture hybride pour construire des pipelines d’IA qui utilisent l’Apprentissage Automatique pour extraire les concepts du domaine et un raisonnement symbolique pour prédire une classification explicable. Le cœur de cette proposition est l’OntoClassifier, un module qui utilise des ontologies de domaine pour générer automatiquement des classifieurs ontologiquement explicables. Nous décrivons l’approche et l’architecture proposées en détaillant l’implémentation et les capacités de l’OntoClassifier. La solution est appliquée en Vision par Ordinateur et est illustrée à l’aide du Pizzaïolo Dataset.
The research in eXplainable Artificial Intelligence (XAI) has emphasized the need to create models based on domain knowledge to make them explainable from their users’ perspective. A significant portion of current work focuses on designing AI models that integrate the domain experts’ semantics and way of reasoning. Drawing inspiration from Concept Based Models and Neuro-Symbolic AI, we propose a hybrid architecture for constructing AI pipelines that utilize Machine Learning to extract domain concepts and symbolic reasoning to predict an explainable classification. The core of this proposal is the OntoClassifier, a module that uses domain ontologies to automatically generate ontologically explainable classifiers. We describe the proposed approach and architecture, detailing the implementation and capabilities of the OntoClassifier. The solution is applied in Computer Vision and is illustrated using the Pizzaïolo Dataset.
Keywords: XAI, hybrid model, knowledge based system, ontology, computer vision
Grégory Bourguin 1 ; Arnaud Lewandowski 1 ; Mourad Bouneffa 1
CC-BY 4.0
@article{ROIA_2025__6_1-2_35_0,
author = {Gr\'egory Bourguin and Arnaud Lewandowski and Mourad Bouneffa},
title = {Hybridation de mod\`eles {d{\textquoteright}IA} avec des classifieurs ontologiquement explicables},
journal = {Revue Ouverte d'Intelligence Artificielle},
pages = {35--57},
year = {2025},
publisher = {Association pour la diffusion de la recherche francophone en intelligence artificielle},
volume = {6},
number = {1-2},
doi = {10.5802/roia.92},
language = {fr},
url = {https://roia.centre-mersenne.org/articles/10.5802/roia.92/}
}
TY - JOUR AU - Grégory Bourguin AU - Arnaud Lewandowski AU - Mourad Bouneffa TI - Hybridation de modèles d’IA avec des classifieurs ontologiquement explicables JO - Revue Ouverte d'Intelligence Artificielle PY - 2025 SP - 35 EP - 57 VL - 6 IS - 1-2 PB - Association pour la diffusion de la recherche francophone en intelligence artificielle UR - https://roia.centre-mersenne.org/articles/10.5802/roia.92/ DO - 10.5802/roia.92 LA - fr ID - ROIA_2025__6_1-2_35_0 ER -
%0 Journal Article %A Grégory Bourguin %A Arnaud Lewandowski %A Mourad Bouneffa %T Hybridation de modèles d’IA avec des classifieurs ontologiquement explicables %J Revue Ouverte d'Intelligence Artificielle %D 2025 %P 35-57 %V 6 %N 1-2 %I Association pour la diffusion de la recherche francophone en intelligence artificielle %U https://roia.centre-mersenne.org/articles/10.5802/roia.92/ %R 10.5802/roia.92 %G fr %F ROIA_2025__6_1-2_35_0
Grégory Bourguin; Arnaud Lewandowski; Mourad Bouneffa. Hybridation de modèles d’IA avec des classifieurs ontologiquement explicables. Revue Ouverte d'Intelligence Artificielle, Post-actes de la conférence Ingénierie des Connaissances (IC 2021-2022-2023), Volume 6 (2025) no. 1-2, pp. 35-57. doi: 10.5802/roia.92
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