Abstract
In this paper, we review two techniques for topic discovery in collections of text documents (Latent Semantic Indexing and K-Means clustering) and present how we integrated them into a system for semi-automatic topic ontology construction. The OntoGen system offers support to the user during the construction process by suggesting topics and analyzing them in real time. It suggests names for the topics in two alternative ways both based on extracting keywords from a set of documents inside the topic. The first set of descriptive keyword is extracted using document centroid vectors, while the second set of distinctive keyword is extracted from the SVM classification model dividing documents in the topic from the neighboring documents.
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Fortuna, B., Mladenič, D., Grobelnik, M. (2006). Semi-automatic Construction of Topic Ontologies. In: Ackermann, M., et al. Semantics, Web and Mining. EWMF KDO 2005 2005. Lecture Notes in Computer Science(), vol 4289. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11908678_8
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DOI: https://doi.org/10.1007/11908678_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47697-9
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