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Project Goal
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To develop innovative methods for information analysis and visualization.
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Researchers/Collaborators
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Hsinchun Chen,
Associate Professor, Management Information Systems, University of Arizona,
Tucson, AZ 85721, (520) 621-4153, hchen@bpa.arizona.edu
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Bruce
R. Schatz, Director of the Digital Library Research Program,
University of Illinois at Urbana-Champaign, Urbana, IL 61801, (217)
244-0651, schatz@csl.ncsa.uiuc.edu
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Research Assistants:
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Key Technical Summary
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CSQuest is a collection of 280,000 computer engineering terms and 10M
links. The associated concept space allows users to identify other relevant
search terms for searching Internet computer engineering servers or homepages
such as the CS Technical
Report Sites.
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The GS-Map analyzes 200 raw Group Systems (GS) electronic meeting comments,
identifies key terms and organizes them in 1-D, 2-D, and 3-D (VRML) displays.
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The Arizona Noun Phraser required 17 serial hours to generate noun
phrases and 2 hours and 40 minutes to create the concept space.
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Techniques:
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Arizona Noun Phraser
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Automatic Indexing
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Concept Space Generation: Batch, Incremental, Real-time
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Kohonen Self-Organization Map (SOM) Algorithms:
* 1-D, 2-D, 3-D (VRML)
displays
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Multi-dimensional Scaling: 1-D, 2-D, 3-D
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Ward's Hierarchical Clustering
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Visualization: Fisheye view, Fractal view
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Intelligent Agents: Java-based genetic algorithm spiders
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Key Results Summary
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| The following studies are currently underway: |
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MDS-categorization of GS comments is being compared to human categorization.
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GS-Map-user studies are being conducted on the 1-D, 2-D, and 3-D Kohonen-based maps.
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SOM vs. Ward's comparison study
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Genetic-algorithm based spider
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Publications
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H. Chen, Y. Chung, M. Ramsey, and C. Yang. A Smart Itsy Bitsy Spider
for the Web, Journal of the American Society for Information Science,
1998, forthcoming.
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H. Chen, A. Houston, R. Sewell, and B. Schatz. Internet Browsing and
Searching: User Evaluations of Category Map and Concept Space
Techniques, Journal of the American Society for Information Science,
1998, forthcoming.
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D. Roussinov and H. Chen. A Scalable Self-Organizing Map Algorithm
for Textual Classification: A Neural Network Approach to Thesaurus
Generation,
accepted for CCAI Communication Cognition and Artificial Intelligence,
Spring 1998.
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