Persistent Link:
http://hdl.handle.net/10150/105489
Title:
Concept-based searching and browsing: a geoscience experiment
Author:
Hauck, Roslin V.; Sewell, Robin R.; Ng, Tobun Dorbin; Chen, Hsinchun
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
Issue Date:
2001
Description:
Artificial Intelligence Lab, Department of MIS, University of Arizona
URI:
http://hdl.handle.net/10150/105489
Submitted date:
2004-10-01
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
Keywords:
Digital Libraries; Information Extraction
Local subject classification:
National Science Digital Library; NSDL; Artificial intelligence lab; AI lab; Information retrieval

Full metadata record

DC FieldValue Language
dc.contributor.authorHauck, Roslin V.en_US
dc.contributor.authorSewell, Robin R.en_US
dc.contributor.authorNg, Tobun Dorbinen_US
dc.contributor.authorChen, Hsinchunen_US
dc.date.accessioned2004-10-01T00:00:01Z-
dc.date.available2010-06-18T23:26:19Z-
dc.date.issued2001en_US
dc.date.submitted2004-10-01en_US
dc.identifier.citationConcept-based searching and browsing: a geoscience experiment 2001, 27(4):199-210 Journal of the American Society for Information Scienceen_US
dc.identifier.urihttp://hdl.handle.net/10150/105489-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractIn 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.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherWiley Periodicals, Incen_US
dc.subjectDigital Librariesen_US
dc.subjectInformation Extractionen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial intelligence laben_US
dc.subject.otherAI laben_US
dc.subject.otherInformation retrievalen_US
dc.titleConcept-based searching and browsing: a geoscience experimenten_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalJournal of the American Society for Information Scienceen_US
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