%0 Report %A Vinay K. Chaudhri and Peter E. Clark and Sunil Mishra and John Pacheco and Aaron Spaulding and Jing Tien %T AURA: Capturing Knowledge and Answering Questions on Science Textbooks %I SRI International %D 2009 %X AURA is an AI-motivated system with a healthy intersec-tion with the sciences: its short-term goal is to enable do-main experts to construct declarative knowledge bases (KBs) from 50 pages of a science textbook for Physics, Chemistry, and Biology in a way that another user can pose questions similar to those in an Advanced Placement (AP) exam and get answers and explanations. In building AURA, and in line with the conference theme of The Inter-disciplinary Reach of AI, a key question and challenge has been: How much of the knowledge in the three domains can be captured through a generic knowledge capture and rea-soning capability and to what extent does it need to be spe-cialized for each domain? This paper contributes an answer to this question based on the experience of designing and implementing AURA. It also presents the first concise dis-tillation of the key ideas in AURA, integrating ideas from previous specialized publications. %U http://www.ai.sri.com/pubs/files/1768.pdf
