SemCaDo: A Serendipitous Strategy for Learning Causal Bayesian Networks Using Ontologies

  • Montassar Ben Messaoud
  • Philippe Leray
  • Nahla Ben Amor
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6717)


Learning Causal Bayesian Networks (CBNs) is a new line of research in the machine learning field. Within the existing works in this direction [8, 12, 13], 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.


Causal Relation Domain Ontology Semantic Distance Ontology Evolution Edge Orientation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Montassar Ben Messaoud
    • 1
    • 2
  • Philippe Leray
    • 2
  • Nahla Ben Amor
    • 1
  1. 1.LARODEC, Institut Supérieur de Gestion TunisLe BardoFrance
  2. 2.Knowledge and Decision Team Laboratoire d’Informatique de Nantes Atlantique (LINA) UMR 6241Ecole Polytechnique de l’Université de NantesFrance

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