Ph.D. Dissertation, Indiana University, 2005.
In traditional views of knowledge management, knowledge capture is seen as primarily knowledge acquisition, capturing knowledge that already exists within the expert. This thesis proposes an alternative approach, knowledge extension, based on the premise that a knowledge model evolves from coordinated processes of knowledge acquisition and knowledge construction. In this view, it is crucial to support experts' construction of new knowledge as they extend existing knowledge models. This dissertation develops and evaluates artificial intelligence methods to facilitate knowledge extension, especially in the context of knowledge modeling via concept mapping. The problem of supporting knowledge extension raises two research questions: First, how can topic descriptors be algorithmically extracted from concept maps, and second, how to use these topic descriptors to identify candidate topics on the Web with the right balance of novelty and relevance. To address these questions, this thesis develops the theoretical framework required for a topic suggester to aid information search in the context of a knowledge model under construction. Finally, it describes and evaluates EXTENDER, an implemented support tool based on this framework. The proposed algorithms have been developed and tested within the framework of CmapTools, a widely-used system for supporting knowledge modeling using concept maps. However, their generality makes them applicable to a broad class of knowledge modeling systems, and to Web search in general.
See http://www.cs.indiana.edu/~leake/INDEX.html for additional publications in David Leake's paper archive.