A Statistical Approach to Incremental Induction of First-Order Hierarchical Knowledge Bases

Authors:
David J. Stracuzzi;Tolga Könik
Affiliations:
School of Computing and Informatics, Arizona State University, Tempe, USA AZ 85287-8809;Center for the Study of Language and Information, Stanford University, Stanford, USA CA 94305
Venue:
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Year:
2008

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Abstract

Knowledge bases play an important role in many forms of artificial intelligence research. A simple approach to producing such knowledge is as a database of ground literals. However, this method is neither compact nor computationally tractable for learning or performance systems to use. In this paper, we present a statistical method for incremental learning of a hierarchically structured, first-order knowledge base. Our approach uses both rules and ground facts to construct succinct rules that generalize the ground literals. We demonstrate that our approach is computationally efficient and scales well to domains with many relations.