Assessing the Suitability of MeSH Ontology for Classifying Medline Documents

  • Rosalía Laza
  • Reyes Pavón
  • Miguel Reboiro-Jato
  • Florentino Fdez-Riverola
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 93)

Abstract

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.

Keywords

document classification MeSH ontology Medline documents Bayesian networks 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Rosalía Laza
    • 1
  • Reyes Pavón
    • 1
  • Miguel Reboiro-Jato
    • 1
  • Florentino Fdez-Riverola
    • 1
  1. 1.ESEI: Escuela Superior de Ingeniería InformáticaUniversity of Vigo, Edificio PolitécnicoOurenseSpain

Personalised recommendations