SemCaDo: a serendipitous strategy for learning causal Bayesian networks using ontologies
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
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Bayesian network (BN) is one of the popular probabilistic methods of diagnosing diseases in e-health applications. However, it is normally a complex task to construct a BN for diagnosing a specific disease by collecting and analyzing domain knowledge. We propose a semi-automatic way of constructing BNs for diagnosing diseases. Our method automatically generates nodes in a BN out of e-health ontology, and allows developers to easily establish links among nodes based on a meta-model that represents cause-and-effect relationships among ontologies.