Authors:
Ahmed Mabrouk
;
Sarra Ben Abbes
;
Lynda Temal
;
Ledia Isaj
and
Philippe Calvez
Affiliation:
LAB CSAI ENGIE, 4 Rue Joséphine Baker, 93240 Stains, France
Keyword(s):
Bayesian Network Structure, Ontology, Expert Knowledge, Learning, Dependencies, Renewable Energies.
Abstract:
Exploiting experts’ domain knowledge represented in the ontology can significantly enhance the quality of the Bayesian network (BN) structure learning. However, in practice, using such information is not a trivial task. In fact, knowledge encompassed in ontologies doesn’t share the same semantics as those represented in a BN. To tackle this issue, a large effort has been devoted to create a bridge between both models. But, as far as we know, most state-of-the-art approaches require a Bayesian network-specific ontology for which the BN structure could be easily derived. In this paper, we propose a generic method that allows deriving knowledge from ontology to enhance the learning process of BN. We provide several steps to infer dependencies as well as orientations of some edges between variables. The proposition is implemented and applied to the wind energy domain.