Effective and efficient knowledge base refinement

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. Fi rstly, the range of misclassified examples handled by KRUST has been augmen ted by the introduction of inductive refinement operators. Secondly, STALKE R's testing phase has been greatly speeded up by using a Truth Maintenance System (TMS). The resulting system is more effective than other refinement systems because it generates many alternative refinements. At the same time , STALKER is very efficient since KRUST's computationally expensive impleme ntation and testing of refined knowledge bases has been replaced by a TMS-b ased simulator.