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Learning Ontologies to Improve Text Clustering and Classification

Learning Ontologies to Improve Text Clustering and Classification

Published: 2006 Februar
Herausgeber: Myra Spiliopoulou, Rudolf Kruse, Andreas Nürnberger, Christian Borgelt, Wolfgang Gaul
Buchtitel: From Data and Information Analysis to Knowledge Engineering: Proceedings of the 29th Annual Conference of the German Classification Society (GfKl 2005), Magdeburg, Germany, March 9-11, 2005
Ausgabe: 30
Reihe: Studies in Classification, Data Analysis, and Knowledge Organization
Seiten: 334-341
Verlag: Springer

Referierte Veröffentlichung


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 clus- tering and classification tasks, whereby the automatically acquired ontologies yield an improvement competitive with the manually engineered ones.

ISBN: 3-540-31313-3
Download: Media:2006_867_Bloehdorn_Learning_Ontolo_1.pdf
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Text Mining, Ontology Learning