Effective and Efficient Knowledge Base Refinement

Authors:
Leonardo Carbonara;Derek Sleeman
Affiliations:
BT UK Markets, British Telecom, pp 411.7, 120 Holborn, London EC1N 2TE, UK. leonardo.carbonara@bt.com;Department of Computing Science, King‘s College, University of Aberdeen, Aberdeen AB24 3UE, UK. dsleeman@csd.abdn.ac.uk
Venue:
Machine Learning
Year:
1999

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Abstract

This paper presents the STALKER knowledge base refinementsystem. Like its predecessor KRUST, STALKER proposes many alternativerefinements to correct the classification of each wrongly classifiedexample in the training set. However, there are two principaldifferences between KRUST and STALKER. Firstly, the range ofmisclassified examples handled by KRUST has been augmented by theintroduction of inductive refinement operators. Secondly,STALKER‘s testing phase has been greatly speeded up by using a TruthMaintenance System (TMS). The resulting system is moreeffective than other refinement systems because it generates manyalternative refinements. At the same time, STALKER is very efficientsince KRUST‘s computationally expensive implementation and testing ofrefined knowledge bases has been replaced by a TMS-based simulator.