Acquiring Bayesian Networks from Text


Olivia Sanchez-Graillet, Massimo Poesio

University of Essex, Department of Computer Science, Wivenhoe Park, Colchester CO4 3SQ, United Kingdom.{osanch, poesio}@essex.ac.uk




Causal inference is one of the most fundamental reasoning processes and one that is essential for question-answering as well as more general AI applications such as decision-making and diagnosis. Bayesian Networks are a popular formalism for encoding (probabilistic) causal knowledge that allows for inference. We developed a system for acquiring causal knowledge from text. Our system identifies sentences that specify causal relations and extracts from them causal patterns, taking into account connectives such as conjunction, disjunction and negation, and recognising causes and effects by analysing terms. The dependencies among the causes and effects found in text can be encoded as Bayesian networks. We evaluated our work by comparing the network structures obtained by our system with the ones created by a human evaluator.


Causal Knowledge acquisition, causal relationships, term extraction, Bayesian Networks, inference.

Language(s) English
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