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    Concept-based searching and browsing: a geoscience experiment

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    Author
    Hauck, Roslin V.
    Sewell, Robin R.
    Ng, Tobun Dorbin
    Chen, Hsinchun
    Issue Date
    2001
    Submitted date
    2004-10-01
    Keywords
    Digital Libraries
    Information Extraction
    Local subject classification
    National Science Digital Library
    NSDL
    Artificial intelligence lab
    AI lab
    Information retrieval
    
    Metadata
    Show full item record
    Citation
    Concept-based searching and browsing: a geoscience experiment 2001, 27(4):199-210 Journal of the American Society for Information Science
    Publisher
    Wiley Periodicals, Inc
    Journal
    Journal of the American Society for Information Science
    Description
    Artificial Intelligence Lab, Department of MIS, University of Arizona
    URI
    http://hdl.handle.net/10150/105489
    Abstract
    In the recent literature, we have seen the expansion of information retrieval techniques to include a variety of different collections of information. Collections can have certain characteristics that can lead to different results for the various classification techniques. In addition, the ways and reasons that users explore each collection can affect the success of the information retrieval technique. The focus of this research was to extend the application of our statistical and neural network techniques to the domain of geological science information retrieval. For this study, a test bed of 22,636 geoscience abstracts was obtained through the NSF/DARPA/NASA funded Alexandria Digital Library Initiative project at the University of California at Santa Barbara. This collection was analyzed using algorithms previously developed by our research group: concept space algorithm for searching and a Kohonen self-organizing map (SOM) algorithm for browsing. Included in this paper are discussions of our techniques, user evaluations and lessons learned.
    Type
    Journal Article (Paginated)
    Language
    en
    Collections
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