Barker, K. and Chaudhri, V., Chaw, Sha Yi and Clark, Peter E. and Fan, James and Israel, David and Mishra, Sunil and Porter, Bruce and Romero, Pedro and Tecuci, Dan and Yeh, Peter. A Question Answering System for AP Chemistry: Assessing KR Technologies, in Proceedings of the 9th International Conference on Knowledge Representation and Reasoning, June 2-4 2004.
Basic research in knowledge representation and reasoning (KR&R) has steadily advanced over the years, but it has been difficult to assess the capability of fielded systems derived from this research. In this paper, we present a knowledge-based question-answering system that we developed as part of a broader effort by Vulcan Inc. to assess KR&R technologies, and the result of its assessment. The challenge problem presented significant new challenges for knowledge representation, compared with earlier such assessments, due to the wide variability of question types that the system was expected to answer. Our solution integrated several modern KR&R technologies, in particular semantically well-defined frame systems, automatic classification methods, reusable ontologies, a methodology for knowledge base construction, and a novel extension of methods for explanation generation. The resulting system exhibited high performance, achieving scores for both accuracy and explanation which were comparable to human performance on similar tests. While there are qualifications to this result, it is a significant achievement and an informative data point about the state of the art in KR&R, […]
Keywords: Artificial Intelligence, Artificial Intelligence Center, AIC