Extracting expertise from experts: Methods for knowledge acquisition

TLDR

Knowledge acquisition is the biggest bottleneck in expert system development, but cognitive science methods can uncover human knowledge structures, which are organized into direct and indirect investigative classes. The authors review and provide criteria and literature sources for all principal knowledge acquisition methods. They describe direct methods such as interviews, questionnaires, task observation, protocol analysis, interruption analysis, closed curves, and inferential flow analysis, and indirect methods including multidimensional scaling, hierarchical clustering, weighted networks, ordered trees, and repertory grid analysis.

Abstract

Abstract: Knowledge acquisition is the biggest bottleneck in the development of expert systems. Fortunately, the process of translating expert knowledge to a form suitable for expert system development can benefit from methods developed by cognitive science to reveal human knowledge structures. There are two classes of these investigative methods, direct and indirect. We provide reviews, criteria for use, and literature sources for all principal methods. Direct methods discussed are: interviews, questionnaires, observation of task performance, protocol analysis, interruption analysis, closed curves, and inferential flow analysis. Indirect methods include: multidimensional scaling, hierarchical clustering, general weighted networks, ordered trees, and repertory grid analysis.