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On Text Mining Algorithms for Automated Maintenance of Hierarchical Knowledge Directory

  • Han-joon Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4092)

Abstract

This paper presents a series of text-mining algorithms for managing knowledge directory, which is one of the most crucial problems in constructing knowledge management systems today. In future systems, the constructed directory, in which knowledge objects are automatically classified, should evolve so as to provide a good indexing service, as the knowledge collection grows or its usage changes. One challenging issue is how to combine manual and automatic organization facilities that enable a user to flexibly organize obtained knowledge by the hierarchical structure over time. To this end, I propose three algorithms that utilize text mining technologies: semi-supervised classification, semi-supervised clustering, and automatic directory building. Through experiments using controlled document collections, the proposed approach is shown to significantly support hierarchical organization of large electronic knowledge base with minimal human effort.

Keywords

Unlabeled Data Knowledge Object Concept Drift Label Training Data Directory Building 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Han-joon Kim
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of SeoulKorea

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