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Learning Causal Bayesian networks from Literature Data

By Péter Antal and András Millinghoffer


Abstract: We propose two machine learning methods based on Bayesian networks to discover automatically real world causal relations from scientific publications. The first method assumes that the occurrence of causal mechanisms (and the corresponding entities) in the publications follows a transitive scheme, the second method assumes that the causal mechanisms occur independently. We perform an evaluation of these methods in the ovarian cancer domain, because of the availability of an expert causal model as gold-standard reference and various collections of scientific publications as source. The evaluation shows that the fully observable transitive model and the intransitive model with hidden variables perform comparable to the performance of a human expert and the second, computationally more complex method allowing hidden variables proved to be slightly better

Topics: Bayesian network, Literature data, Learning
Year: 2009
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
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