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Publication Details

Graph-based Acquisition of Expressive Knowledge

by Chaudhri, V and Murray, K and Pacheco, J and Clark, P and Porter, B and Hayes, P

EKAW , 2004.

Abstract

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.

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AIC Personnel

Name Title E-mail
Chaudhri, Vinay K Program Director
Clark, Peter E. Allen Institute of Artificial Intelligence
Hayes, Pat Senior Research Scientist, Univ of West Florida
Murray, Ken S Senior Computer Scientist
Pacheco, John Research Engineer
Porter, Bruce Professor, University of Texas at Austin

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