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Integrating Ontological Knowledge for Iterative Causal Discovery and Visualization

  • Montassar Ben Messaoud
  • Philippe Leray
  • Nahla Ben Amor
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5590)

Abstract

Bayesian networks (BN) have been used for prediction or classification tasks in various domains. In the first applications, the BN structure was causally defined by expert knowledge. Then, algorithms were proposed in order to learn the BN structure from observational data. Generally, these algorithms can only find a structure encoding the right conditional independencies but not all the causal relationships. Some new domains appear where the model will only be learnt in order to discover these causal relationships. To this end, we will focus on discovering causal relations in order to get Causal Bayesian Networks (CBN). To learn such models, interventional data (i.e. samples conditioned on the particular values of one or more variables that have been experimentally manipulated) are required. These interventions are usually very expensive to perform, therefore the choice of variables to experiment on can be vital when the number of experimentations is restricted. In many cases, available ontologies provide high level knowledge for the same domain under study. Consequently, using this semantical knowledge can turn out of a big utility to improve causal discovery. This article proposes a new method for learning CBNs from observational data and interventions. We first extend the greedy approach for perfect observational and experimental data proposed in [13], by adding a new step based on the integration of ontological knowledge, which will allow us to choose efficiently the interventions to perform in order to obtain the complete CBN. Then, we propose an enriched visualization for better understanding of the causal graphs.

Keywords

Bayesian Network Directed Acyclic Graph Semantic Similarity Semantic Relatedness Causal Model 
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 2009

Authors and Affiliations

  • Montassar Ben Messaoud
    • 1
  • Philippe Leray
    • 2
  • Nahla Ben Amor
    • 1
  1. 1.LARODEC, Institut Supérieur de Gestion TunisLe BardoTunisie
  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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