An iterative approach to build relevant ontology-aware data-driven models - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Information Sciences Year : 2013

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


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.
Fichier principal
Vignette du fichier
INS_TreeOnto_compiled.pdf (607.21 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-00753335 , version 1 (19-11-2012)



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⟩
471 View
725 Download



Gmail Facebook X LinkedIn More