Adding new information to an existing knowledge base can have significant consequences. For example, new information might contradict existing knowledge or reveal a "gap" in the knowledge base. Most approaches to knowledge-base refinement either ignore these consequences or compute them exhaustively. Our approach, formalized in a task called knowledge integration, is to partially elaborate the consequences of new information. A form of domain knowledge called `views' controls the search to identify non-superficial consequences of new information. A prototype knowledge integration program has been implemented and demonstrated with a complex extension to a large knowledge base.

In Proceedings of the Sixth International Workshop on Machine Learning, pp. 290-295, Ithaca, NY, June 1989.

@InProceedings{murray:ml89, title={Controlling Search for the Consequences of New Information during Knowledge Integration}, author={K. Murray and Bruce Porter }, booktitle={Proceedings of the Sixth International Workshop on Machine Learning}, month={June}, address={Ithaca, NY}, key={KI}, pages={290-295}, url="http://www.cs.utexas.edu/users/ai-lab?murray:ml89", year={1989} }