Automatic Classification for the Identification of Relationships in a Meta-Data Repository

  • Gerd Beuster
  • Ulrich Furbach
  • Margret Gross-Hardt
  • Bernd Thomas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2843)


For a large company a prototype for automatic detection of similar objects in database systems has been developed. This task has been accomplished by transferring the database object classification problem into a text classification problem and applying standard classification algorithms. Although the data provided for the task did not look promising due to the small number of positive examples, the results turned out to be very good.


Vector Representation Object Type Data Repository Relationship Type Database Object 
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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  1. 1.
    Beuster, G.: MIC — A System for Classification of Structured and Unstructured Texts. Master’s thesis, University Koblenz (2001),
  2. 2.
    Bouguettaya, A., Benatallah, B., Elmagarmid, A.K.: Interconnecting Heterogeneous Information Systems. Kluwer Academic Publishers, Dordrecht (1998)Google Scholar
  3. 3.
    Chinchor, N.: Muc-4 evaluation metrics. In: Fourth Message Understanding Conference, pp. 22–29. Morgan Kaufmann, San Francisco (1992)CrossRefGoogle Scholar
  4. 4.
    Marco, D.: Building and Managing the Meta Data Repository: A Full Lifecycle Guide. John Wiley & Sons, Chichester (2000)Google Scholar
  5. 5.
    Maron, M.: Automatic indexing: An experimental inquiry. Journal of the ACM (JACM) 8, 404–417 (1961)zbMATHCrossRefGoogle Scholar
  6. 6.
    Mitchell, T.M.: Machine Learning. McGraw-Hill International Editions (1997)Google Scholar
  7. 7.
    Quinlan, J.: Discovering rules by induction from a large collection of examples. In: Michie, D. (ed.) Expert systems in the Micro-Electronic Age, pp. 168–201. Edinburgh University Press, Edinburgh (1979)Google Scholar
  8. 8.
    Rumelhart, D.D., Hinton, G.E., Williams, R.J.: Learning representations by backpropagating errors. Nature, 533–536 (1986)Google Scholar
  9. 9.
    Shannon, C.: A mathematical theory of communication. Bell System Technical Journal 27, 379–423 (1948)zbMATHMathSciNetGoogle Scholar
  10. 10.
    Sheth, A., Larson, J.: Federated database systems for managing distributed, heterogeneous, and autonomous databases. ACM Computing Surveys 22, 183–236 (1990)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Gerd Beuster
    • 1
  • Ulrich Furbach
    • 1
  • Margret Gross-Hardt
    • 1
  • Bernd Thomas
    • 1
  1. 1.Institut für InformatikUniversität Koblenz-Landau 

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