  This paper outlines a methodology for analyzing the representational support
for knowledge-based decision-modeling in a broad domain. A relevant set of
inference patterns and knowledge types are identified. By comparing the
analysis results to existing representations, some insights are gained into a
design approach for integrating categorical and uncertain knowledge in a
context sensitive manner.
