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Where Does Text Mining Meet Knowledge Management? A Case Study

  • E. D’Avanzo
  • A. Elia
  • T. Kuflik
  • A. Lieto
  • R. Preziosi

Abstract

Knowledge management in organizations is about ensuring that the right information is delivered to the right person on the right time. How can the right information be easily identified? This work demonstrates how text mining provides a tool for generating human understandable textual summaries that ease the task of finding the relevant information within organizational documents repositories.

Keywords

Knowledge Management Natural Language Processing Knowledge Management System Concept Hierarchy Biomedical Ontology 
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

  1. 1.
    Bordoni, L. and D’Avanzo, E. (2002). Prospects for Integrating Text Mining and Knowledge Management. The IPTS Report (Institute for Prospective Technological Studies), Vol. 68.Google Scholar
  2. 2.
    Nonaka, I. (1991). The knowledge creating company. Harvard Business Review, 69:96-104.Google Scholar
  3. 3.
    Day, R.E. (2005). Clearing Up “Implicit Knowledge”: Implications for Knowledge Man-agement, Information Science, Psychology and Social Epistemology, Wiley-Interscience, New York.Google Scholar
  4. 4.
    Rajman, M. and Besançon, R. (1997). Text Mining: Natural Language Techniques and Text Mining Applications, Proceedings of the 7th IFIP 2.6 Working Conference on Database Se-mantics (DS-7).Google Scholar
  5. 5.
    Feldman, R., Fresko, M., Hirsh, H., Aumann, Y., Lipshat, O., Schler, Y., and Rajman, M. (1998). Knowledge Management: A Text Mining Approach, Proceedings of the 2nd International Conference on Practical Aspects of Knowledge Management, 29-30.Google Scholar
  6. 6.
    Mooney, R. J. and Bunescu, R. (2005). Mining Knowledge from Text Using Information Extraction, SIGKDD Explorations (special issue on Text Mining and Natural Language Processing), 7(1), 3-10.Google Scholar
  7. 7.
    Litowsky, K. C. (2005). CL Research’s Knowledge Management System, Prooceedings of the ACL Interactive Poster and Demonstration Session, 13-16.Google Scholar
  8. 8.
    Dey, L., Rastogi, A. C., and Kumar, S. (2006). Generating Concept Ontologies Through Text Mining, Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelli-gence.Google Scholar
  9. 9.
    Doherty P., Lukaszewics, W., and Szalas, A. (2003). Tolerance Spaces and Approximative Representational Structure, Proceedings of the 26th German Conference on Artificial Intelligence, volume 281 of LNAI.Google Scholar
  10. 10.
    Inniss, T. R., Lee, J. R., Light, M., Grassi, M. A., Thomas, G., and Williams, A. B. (2006). Towards Applying Text Mining and Natural Language Processing for Biomedical Ontology Acquisition, Proceedings of the 1st International Workshop on Text Mining in Bioinformat-ics, 7-14.Google Scholar
  11. 11.
    Caropreso, M. F., Matwin, S., and Sebastiani, F. (2001). A learner-independent evaluation of the usefulness of statistical phrases for automated text categorization. In A. G. Chin (Ed.), Text Databases and Document Management: Theory and Practice (pp. 78-102) Hershey (US) Idea Group Publishing.Google Scholar
  12. 12.
    Turney, P. D. (1999). Learning to extract keyphrases from text. Technical Report ERB-1057. (NRC #41622), National Research Council, Institute for Information TechnologyGoogle Scholar
  13. 13.
    Turney, P. D. (2000). Learning algorithms for keyphrase extraction. Information Retrieval, 2(4):303-336CrossRefGoogle Scholar
  14. 14.
    Turney, P. D. (1997). Extraction of keyphrases from text: Evaluation of four algorithms. Tech-nical Report ERB-1051. (NRC #41550), National Research Council, Institute for Information Technology.Google Scholar
  15. 15.
    D’Avanzo, E. and Magnini, B. (2005). A Keyphrase-Based Approach to Summarization: the LAKE System at DUC-2005. DUC Workshop, Proceedings of Human Language Tech-nology Conference/Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP 2005).Google Scholar
  16. 16.
    D’Avanzo, E., Lavelli, A., Magnini, B., and Zanoli, R. (2003). Using Keyphrases as Features for Text Categorization. ITC-irst, Technical report, 12 pp. (Ref. No.: T03-11-01).Google Scholar

Copyright information

© Physica-Verlag Heidelberg 2008

Authors and Affiliations

  • E. D’Avanzo
    • 1
  • A. Elia
    • 1
  • T. Kuflik
    • 2
  • A. Lieto
    • 1
  • R. Preziosi
    • 1
  1. 1.Università di SalernoFiscianoItaly
  2. 2.The University of HaifaHaifaIsrael

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