Effective and Efficient Knowledge Base Refinement

  title={Effective and Efficient Knowledge Base Refinement},
  author={Leonardo Carbonara and Derek H. Sleeman},
  journal={Machine Learning},
This paper presents the STALKER knowledge base refinement system. Like its predecessor KRUST, STALKER proposes many alternative refinements to correct the classification of each wrongly classified example in the training set. However, there are two principal differences between KRUST and STALKER. Firstly, the range of misclassified examples handled by KRUST has been augmented by the introduction of inductive refinement operators. Secondly, STALKER's testing phase has been greatly speeded up by… CONTINUE READING
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