Experience-Based Support for Human-Centered Knowledge
Modeling
David Leake, Ana Maguitman, Thomas Reichherzer
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.
Citation
Leake, David, Maguitman, Ana, and Reichherzer,
Thomas. Experience-Based Support for Human-Centered Knowledge
Modeling. Knowelege-Based Systems, 68(1): 77--87, 2014.
Full
paper on Elsevier ScienceDirect