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Publication in EndNote Format

%0 Journal Article %A Chaudhri, V and Murray, K and Pacheco, J and Clark, P and Porter, B and Hayes, P %T Graph-based Acquisition of Expressive Knowledge %B EKAW %D 2004 %X Capturing and exploiting knowledge is at the heart of several important problems such as decision-making, the semantic web, and intelligent agents. The captured knowledge must be accessible to sub-ject matter experts so that the knowledge can be easily extended, queried, and debugged. In our pre-vious work to meet this objective, we created a knowledge-authoring system based on graphical assembly from components that allowed acquisition of an interestingly broad class of axioms. In this paper, we explore the question: can we expand the axiom classes acquired by building on our existing graphical methods and still retain simplicity so that people with minimal training in knowledge repre-sentation can use it? Specifically, we present tech-niques used to capture ternary relations, classifica-tion rules, constraints, and if-then rules. %U http://www.ai.sri.com/pubs/files/1013.pdf

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