Literature Mining using Bayesian Networks.

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