Discovering regularities from knowledge bases

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 paper addresses the problem of discovering regularities in large knowledge bases that contain many assertions in diierent domains. The paper begins with a deenition of regularities and gives the motivation for such a deenition. 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 connrming hypotheses, its application to some real knowledge base has shown some encouraging and interesting results.