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Visualizing Knowledge Domains
Katy Borner, Chaomei Chen, Kevin Boyack
School of Library and Information Science, Indiana University
Project Description:
Advanced data mining and information visualization techniques can be applied to support science and technology management. Large amounts of, e.g., publication, patent, and grant data are analyzed, correlated, and visualized to map the semantic space of researchers, publications, funding, etc.. The resulting visualizations can be utilized to objectively identify major research areas, experts, institutions, grants, publications, journals, etc. in a research area of interest. In addition, they can assist identify interconnections, the import and export of research between fields, the dynamics (speed of growth, diversification) of scientific fields, scientific and social networks, and the impact of strategic and applied research funding programs among others.

Using a tutorial style, various algorithms were applied to map papers related to "Visualizing Knowledge Domain" research, the so called ARIST data set. Never before have so many knowledge domain mapping algorithms been applied to one data set.

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