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• W27336301 abstract "In biomedical domains, free text electronic literature is an important resource for knowledge discovery and acquisition, particularly to provide a priori components for evaluating or learning domain models. Aiming at the automated extraction of this prior knowledge we discuss the types of uncertainties in a domain with respect to causal mechanisms, formulate assumptions about their report in scientific papers and derive generative probabilistic models for the occurrences of biomedical concepts in papers. These results allow the discovery and extraction of latent causal dependency relations from the domain literature using minimal linguistic support. Contrary to the currently prevailing methods, which assume that relations are sufficiently formulated for linguistic methods, our approach assumes only the report of causally associated entities without their tentative status or relations, and can discover new relations and prune redundancies by providing a domain-wide model. Therefore the proposed Bayesian network based text mining is an important complement to the linguistic approaches." @default.
• W27336301 created "2016-06-24" @default.
• W27336301 creator A5005566868 @default.
• W27336301 creator A5041069751 @default.
• W27336301 date "2006-01-01" @default.
• W27336301 modified "2023-09-23" @default.
• W27336301 title "Literature Mining using Bayesian Networks." @default.
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