Effective and Efficient Knowledge Base Refinement

@article{Carbonara1999EffectiveAE,
  title={Effective and Efficient Knowledge Base Refinement},
  author={Leonardo Carbonara and Derek H. Sleeman},
  journal={Machine Learning},
  year={1999},
  volume={37},
  pages={143-181}
}
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
Highly Cited
This paper has 26 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 18 extracted citations

References

Publications referenced by this paper.

Similar Papers

Loading similar papers…