An iterative approach to build relevant ontology-aware data-driven models

Rallou Thomopoulos (1, 2) , Sébastien Destercke (3, 4) , Brigitte Charnomordic (5) , Johnson Iyan (2, 5) , Joel Abecassis (2)

1 GRAPHIK - Graphs for Inferences on Knowledge
2 UMR IATE - Ingénierie des Agro-polymères et Technologies Émergentes
3 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]
4 Labex MS2T - Laboratoire d'Excellence "Maîtrise des Systèmes de Systèmes Technologiques"
5 MISTEA - Mathématiques, Informatique et STatistique pour l'Environnement et l'Agronomie

In many fields involving complex environments or living organisms, data-driven models are useful to make simulations in order to extrapolate costly experiments and to design decision-support tools. Learning methods can be used to build interpretable models from data. However, to be really useful, such models must be trusted by their users. From this perspective, the domain expert knowledge can be collected and modelled to help guiding the learning process and to increase the confidence in the resulting models, as well as their relevance. Another issue is to design relevant ontologies to formalize complex knowledge. Interpretable predictive models can help in this matter. In this paper, we propose a generic iterative approach to design ontology-aware and relevant data-driven models. It is based upon an ontology to model the domain knowledge and a learning method to build the interpretable models (decision trees in this paper). Subjective and objective evaluations are both involved in the process. A case study in the domain of Food Industry demonstrates the interest of this approach.

Mots clés: Machine learning; Ontology; Expert knowledge; Knowledge integration; Classification tree

Domaines: Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]

Rallou Thomopoulos, Sébastien Destercke, Brigitte Charnomordic, Johnson Iyan, Joel Abecassis. An iterative approach to build relevant ontology-aware data-driven models. Information Sciences, 2013, 221, pp.452-472. ⟨10.1016/j.ins.2012.09.015⟩. ⟨hal-00753335⟩