Andrew Goldenkranz

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In the fall 2010 issue of the AI Magazine, we reported the design, implementation and evaluation of a knowledge acquisition system called AURA. AURA enables domain experts in Physics, Chemistry and Biology to author their knowledge, and a different set of experts to pose questions against that knowledge. The evaluation results previously reported were from(More)
BACKGROUND Using knowledge representation for biomedical projects is now commonplace. In previous work, we represented the knowledge found in a college-level biology textbook in a fashion useful for answering questions. We showed that embedding the knowledge representation and question-answering abilities in an electronic textbook helped to engage student(More)
Our task is to create a taxonomy from an AP Biology textbook’s glossary terms [1] for Project Halo [2]. Project Halo’s goal is to build a reasoning system capable of answering novel questions and solving advanced problems in a broad range of scientific disciplines. In support of this goal, the resulting taxonomy is to be used as a foundation for translating(More)
To scale the knowledge base of a Biology textbook from 50 pages to 300 pages in the context of Project Halo, we have formulated a knowledge factory process [1]. The process involves a sentence-based encoding strategy under which a domain expert examines each sentence in the text-book [2] and represents it in a knowledge base (KB) as best as it can be(More)
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