Improving semantic integration by learning semantic interpretation rules
Michael Glaß, Bruce Porter · 2008
When extending a scientific knowledge base with new information, particularly information presented in nat-ural language, it is important that the information be encoded in a form that is compatible with the existing knowledge base. Hand built systems for semantic in-terpretation and knowledge integration can suffer from brittleness. Methods for learning semantic interpreta-tion and integration exist, but typically require large numbers of aligned training examples. Our approach to semantic integration learns rules mapping from syn-tactic forms to semantic forms using a knowledge base and a text corpus from the same domain. A Framework for Scientific Knowledge Integration