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Mining Knowledge from Text Collections Using Automatically Generated Metadata

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Practical Aspects of Knowledge Management (PAKM 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2569))

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Abstract

Data mining is typically applied to large databases of highly structured information in order to discover new knowledge. In businesses and institutions, the amount of information existing in repositories of text documents usually rivals or surpasses the amount found in relational databases. Though the amount of potentially valuable knowledge contained in document collections can be great, they are often dificult to analyze. Therefore, it is important to develop methods to efficiently discover knowledge embedded in these document repositories. In this paper we describe an approach for mining knowledge from text collections by applying data mining techniques to metadata records generated via automated text categorization. By controlling the set of metadata fields as well as the set of assigned categories we can customize the knowledge discovery task to address specific questions. As an example, we apply the approach to a large collection of product reviews and evaluate the performance of the knowledge discovery.

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Author information

Authors and Affiliations

  1. Interwoven, Inc., 101 2nd Street, 4th Floor, 94105, San Francisco, CA

    John M. Pierre

Authors
  1. John M. Pierre
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Editors and Affiliations

  1. Department of Knowledge Engineering, University of Vienna, Brünner Str. 72, 1210, Vienna, Austria

    Dimitris Karagiannis

  2. Business Operation Systems, Esslenstr. 3, 8280, Kreuzlingen, Switzerland

    Ulrich Reimer

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© 2002 Springer-Verlag Berlin Heidelberg

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Pierre, J.M. (2002). Mining Knowledge from Text Collections Using Automatically Generated Metadata. In: Karagiannis, D., Reimer, U. (eds) Practical Aspects of Knowledge Management. PAKM 2002. Lecture Notes in Computer Science(), vol 2569. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36277-0_47

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  • DOI: https://doi.org/10.1007/3-540-36277-0_47

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00314-4

  • Online ISBN: 978-3-540-36277-7

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