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.
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