Abstract
Knowledge visualization is a creative process, but difficult to formalize. This paper presents a system that is capable of analyzing voluminous citation data and visualizing the result. The system offers visualizations of trends by clustering scientific papers taken from the web (CiteSeer papers). Two methods are implemented: factor analysis and PFNET. An experiment has been carried out with the literature in knowledge management. A deep analysis of current trends in KM is then performed to check the relevance of these results. While the topical content is specific to knowledge engineering, semantic web, and related sub-areas, the approach could be applied to any general topic area in AI. © Springer-Verlag Berlin Heidelberg 2007.
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CITATION STYLE
Lee, M. R., & Chen, T. T. (2007). Visualizing trends in knowledge management. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4798 LNAI, pp. 362–371). Springer Verlag. https://doi.org/10.1007/978-3-540-76719-0_36
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