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dc.contributor.authorChen, Hsinchun
dc.contributor.authorSchatz, Bruce R.
dc.contributor.authorYim, Tak
dc.contributor.authorFye, David
dc.date.accessioned2004-09-20T00:00:01Z
dc.date.available2010-06-18T23:23:42Z
dc.date.issued1995-04en_US
dc.date.submitted2004-09-20en_US
dc.identifier.citationAutomatic Thesaurus Generation for an Electronic Community System 1995-04, 46(3):175-193 Journal of the American Society for Information Scienceen_US
dc.identifier.urihttp://hdl.handle.net/10150/105321
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractThis research reports an algorithmic approach to the automatic generation of thesauri for electronic community systems. The techniques used included term filtering, automatic indexing, and cluster analysis. The testbed for our research was the Worm Community System, which contains a comprehensive library of specialized community data and literature, currently in use by molecular biologists who study the nematode worm C. elegans. The resulting worm thesaurus included 2709 researchers’ names, 798 gene names, 20 experimental methods, and 4302 subject descriptors. On average, each term had about 90 weighted neighboring terms indicating relevant concepts. The thesaurus was developed as an online search aide. We tested the worm thesaurus in an experiment with six worm researchers of varying degrees of expertise and background. The experiment showed that the thesaurus was an excellent “memory-jogging” device and that it supported learning and serendipitous browsing. Despite some occurrences of obvious noise, the system was useful in suggesting relevant concepts for the researchers’ queries and it helped improve concept recall. With a simple browsing interface, an automatic thesaurus can become a useful tool for online search and can assist researchers in exploring and traversing a dynamic and complex electronic community system.
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherWiley Periodicals, Incen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectIndexingen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial intelligence laben_US
dc.subject.otherAI laben_US
dc.titleAutomatic Thesaurus Generation for an Electronic Community Systemen_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalJournal of the American Society for Information Scienceen_US
refterms.dateFOA2018-08-14T12:58:18Z
html.description.abstractThis research reports an algorithmic approach to the automatic generation of thesauri for electronic community systems. The techniques used included term filtering, automatic indexing, and cluster analysis. The testbed for our research was the Worm Community System, which contains a comprehensive library of specialized community data and literature, currently in use by molecular biologists who study the nematode worm C. elegans. The resulting worm thesaurus included 2709 researchers’ names, 798 gene names, 20 experimental methods, and 4302 subject descriptors. On average, each term had about 90 weighted neighboring terms indicating relevant concepts. The thesaurus was developed as an online search aide. We tested the worm thesaurus in an experiment with six worm researchers of varying degrees of expertise and background. The experiment showed that the thesaurus was an excellent “memory-jogging” device and that it supported learning and serendipitous browsing. Despite some occurrences of obvious noise, the system was useful in suggesting relevant concepts for the researchers’ queries and it helped improve concept recall. With a simple browsing interface, an automatic thesaurus can become a useful tool for online search and can assist researchers in exploring and traversing a dynamic and complex electronic community system.


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