Assessing the Suitability of MeSH Ontology for Classifying Medline Documents
Automated document classification has become an interesting research field due to the increasing availability of biomedical information in digital form which is necessary to catalogue and organize. In this context, the machine learning paradigm is usually applied to text classification, according to which a general inductive process automatically builds a text classifier from a set of pre-classified documents. In this work we investigate the application of a Bayesian network model for the triage of documents represented by the association of different MeSH terms. Our results show both that Bayesian networks are adequate for describing conditional independencies between MeSH terms and that MeSH ontology is a valuable resource for representing Medline documents at different abstraction levels.
Keywordsdocument classification MeSH ontology Medline documents Bayesian networks
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