SemCaDo: a serendipitous strategy for learning causal bayesian networks using ontologies

Montassar Ben Messaoud (1, 2) , Philippe Leray (2) , Nahla Ben Amor (1)

1 LARODEC - Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus
2 LINA - Laboratoire d'Informatique de Nantes Atlantique

Learning Causal Bayesian Networks (CBNs) is a new line of research in the machine learning eld. Within the existing works in this direction, few of them have taken into account the gain that can be expected when integrating additional knowledge during the learning process. In this paper, we present a new serendipitous strategy for learning CBNs using prior knowledge extracted from ontologies. The integration of such domain's semantic information can be very useful to reveal new causal relations and provide the necessary knowledge to anticipate the optimal choice of experimentations. Our strategy also supports the evolving character of the semantic background by reusing the causal discoveries in order to enrich the domain ontologies.

Mots clés: CBNs; Ontologies; Serendipity; Causal discovery; Experimentations; Ontology evolution

Domaines: Intelligence artificielle [cs.AI]

Montassar Ben Messaoud, Philippe Leray, Nahla Ben Amor. SemCaDo: a serendipitous strategy for learning causal bayesian networks using ontologies. The 11th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Jun 2011, Belfast, Ireland. pp.182-193. ⟨hal-00596260⟩