DLGref2: Techniques for Inductive Knowledge Refinement

This paper describes and evaluates machine learning techniques for knowledge-base refinement. These techniques are central to Einstein, a knowledge acquisition system that enables a human expert to collaborate with a machine learning system at all stages of the knowledge-acquisition cycle. Experimental evaluation demonstrates that the knowledge-base refinement techniques are able to significantly increase the accuracy of nontrivial expert systems in a wide variety of domains.