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Learning Ontologies to Improve Text Clustering and Classification

  • Stephan Bloehdorn
  • Philipp Cimiano
  • Andreas Hotho
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Recent work has shown improvements in text clustering and classification tasks by integrating conceptual features extracted from ontologies. In this paper we present text mining experiments in the medical domain in which the ontological structures used are acquired automatically in an unsupervised learning process from the text corpus in question. We compare results obtained using the automatically learned ontologies with those obtained using manually engineered ones. Our results show that both types of ontologies improve results on text clustering and classification tasks, whereby the automatically acquired ontologies yield a improvement competitive with the manually engineered ones.

Keywords

Noun Phrase Latent Semantic Analysis Formal Concept Analysis Text Corpus Concept Hierarchy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin · Heidelberg 2006

Authors and Affiliations

  • Stephan Bloehdorn
    • 1
  • Philipp Cimiano
    • 1
  • Andreas Hotho
    • 2
  1. 1.Institute AIFBUniversity of KarlsruheKarlsruheGermany
  2. 2.KDE GroupUniversity of KasselKasselGermany

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