Enabling domain experts to convey questions to a machine: a modified, template-based approach

  • Authors:

  • Peter Clark;Vinay Chaudhri;Sunil Mishra;Jérôme Thoméré;Ken Barker;Bruce Porter

  • Affiliations:

  • Boeing Phantom Works, Seattle, WA;SRI International, Menlo Park, CA;SRI International, Menlo Park, CA;SRI International, Menlo Park, CA;University of Texas, Austin, TX;University of Texas, Austin, TX

  • Venue:
  • Proceedings of the 2nd international conference on Knowledge capture
  • Year:
  • 2003

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Abstract

In order for a knowledge capture system to be effective, it needs to not only acquire general domain knowledge from experts, but also capture the specific problem-solving scenarios and questions which those experts are interested in solving using that knowledge. For some tasks, this latter aspect of knowledge capture is straightforward. In other cases, in particular for systems aimed at a wide variety of tasks, the question-posing aspect of knowledge capture can be a challenge in its own right. In this paper, we present the approach we have developed to address this challenge, based on the creation of a catalog of domain-independent question types and the extension of question template methods with graphical tools. Our goal was that domain experts could directly convey complex questions to a machine, in a form which it could then reason with. We evaluated the resulting system over several weeks, and in this paper we report some important lessons learned from this evaluation, revealing several interesting strengths and weaknesses of the approach.