Bloehdorn, S.; Cimiano, P. & Hotho, A.: Learning Ontologies to Improve Text Clustering and Classification. From Data and Information Analysis to Knowledge Engineering. Springer Berlin Heidelberg, 2006, S. 334-341
[Volltext]
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.
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@incollection{bloehdorn2006learning,
author = {Bloehdorn, Stephan and Cimiano, Philipp and Hotho, Andreas},
title = {Learning Ontologies to Improve Text Clustering and Classification},
booktitle = {From Data and Information Analysis to Knowledge Engineering},
publisher = {Springer Berlin Heidelberg},
year = {2006},
pages = {334--341},
url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2006/2006-03-gfkl05-bloehdorn-etal-learning-ontologies.pdf},
doi = {http://dx.doi.org/10.1007/3-540-31314-1_40},
isbn = {978-3-540-31313-7},
keywords = {classification, text, clustering, 2006, ol, myown},
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.
-}
}