Experience-based support for human-centered knowledge modeling

Abstract

The construction, capture and sharing of human knowledge is one of the fundamental problems of human-centered computing. Electronic concept maps have proven to be a useful vehicle for building knowledge models. However, the user has to deal with the difficult task of deciding what information to include in these models. This article reports the culmination of a multi-year research project aimed at developing intelligent suggesters designed to aid users of concept mapping tools as they build their knowledge models. It describes Discerner and Extender, two proactive suggesters that can be incorporated into the CmapTools concepts mapping system. Discerner applies case-based reasoning techniques to suggest potentially useful propositions mined from other users’ knowledge models, while Extender mines search engines to suggest new related areas to model. The article presents experimental results addressing two previously open questions for the project: Discerner’s retrieval accuracy and Extender’s ability to generate artificial topics with content similar to topics determined by domain experts. Both experiments show satisfactory results.