| Abstract: | Knowledge bases open new horizons for machine learning research. One challenge is to design learning programs to expand the knowledge base using the knowledge that is currently available. This article addresses the problem of discovering regularities in large knowledge bases that contain many assertions in different domains. the article begins with a definition of regularities and gives the motivation for such a definition. It then outlines a framework that attempts to integrate induction with knowledge. Although the implementation of the framework currently uses only a statistical method for confirming hypotheses, its application to a real knowledge base has shown some encouraging and interesting results. © 1992 John Wiley & Sons, Inc. |
