Automated knowledge acquisition for Prospector-like expert systems

  • Petr Berka
  • Jiří Ivánek
Extended Abstracts
Part of the Lecture Notes in Computer Science book series (LNCS, volume 784)


The method for automatic knowledge acquisition from categorical data is explained. Empirical implications are generated from data according to their frequencies. Only those of them are inserted to created knowledge base whose validity in data statistically significantly differs from the weight composed by the Prospector like inference mechanism from the weights of the implications already present in the base. A comparison with classical machine learning algorithms is discussed. The method is implemented as a part of the Knowledge EXplorer system.


Knowledge Acquisition Input Attribute Inference Mechanism Empirical Implication Knowledge EXplorer 
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 1994

Authors and Affiliations

  • Petr Berka
    • 1
  • Jiří Ivánek
    • 1
  1. 1.Dept. of Information and Knowledge EngineeringPrague University of EconomicsPrague

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