Visualizing a Knowledge Domain's Intellectual Structure

  • Authors:

  • Chaomei Chen;Ray J. Paul

  • Affiliations:

  • -;-

  • Venue:
  • Computer
  • Year:
  • 2001

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Abstract

To make knowledge visualizations clear and easy to interpret, the authors have developed a method that extends and transforms traditional author co-citation analysis (ACA) by extracting structural patterns from the scientific literature and representing them in a 3D knowledge landscape. Integrating citation and co-citation patterns provides a rich, ecological representation of a knowledge domain. Users can apply visualizations to discover patterns and make valuable connections among data. The authors' approach extends conventional ACA by integrating structured modeling and information visualization techniques to provide a 3D knowledge landscape based on citation patterns.Their four-step procedure introduces Pathfinder network scaling to replace multidimensional scaling. It also integrates Pathfinder and factor analysis to visualize specialties in the underlying domain knowledge and visualizes the citation frequency of scientists to track changes in their influence over time.This knowledge visualization approach identifies intellectual groupings based on extending the traditional ACA, augmenting the existing document- and concept-centered approaches to knowledge visualization. The 3D knowledge landscape has practical implications in knowledge visualization, digital libraries, domain analysis, and subject domains, providing powerful tools for tracking intuitively scientific knowledge.