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Automatically Detecting and Organizing Documents into Topic Hierarchies: A Neural Network Based Approach to Bookshelf Creation and Arrangement

  • Andreas Rauber
  • Michael Dittenbach
  • Dieter Merkl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1923)

Abstract

With the increasing amount of information available in el- ectronic document collections, methods for organizing these collections to allow topic-oriented browsing and orientation gain importance. The SOMLib Digital Library System provides such an organization based on the self-organizing map, a popular neural network model. In this pa- per, we present the GHSOM, which, based on the same concepts, allows an automatic hierarchical decomposition and organization of documents, which very intuitively reflects the organization typically found in (ma- nually organized) conventional libraries. We present a case study based on a 3-month article collection from an Austrian daily newspaper.

Keywords

Digital Library Document Collection Organize Document Hierarchical Feature Topic 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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References

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    A. Rauber and D. Merkl. Providing topically sorted access to subsequently released newspaper editions or: How to build your private digital library. In Proc. 11th Int’l Conf. on Database and Expert Systems Applications (DEXA00), Greenwich, UK, 2000.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Andreas Rauber
    • 1
  • Michael Dittenbach
    • 1
  • Dieter Merkl
    • 1
  1. 1.Department of Software TechnologyVienna University of TechnologyWienAustria

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