Automatic Thesaurus Generation for an Electronic Community System

@article{Chen95atg, abstract = {This 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.}, author = {Chen and Hsinchun and Schatz and Bruce, R. and Yim and Tak and Fye and David}, citeulike-article-id = {2251425}, journal = {Journal of the American Society for Information Science}, keywords = {thesaurus}, posted-at = {2008-01-18 15:45:12}, priority = {2}, school = {Artificial Intelligence Lab}, title = {Automatic Thesaurus Generation for an Electronic Community System}, url = {http://dlist.sir.arizona.edu/497/}, year = {1995} }

TY - JOUR ID - Chen95atg L3 - citeulike-article-id:2251425 TI - Automatic Thesaurus Generation for an Electronic Community System JF - Journal of the American Society for Information Science N2 - This 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. KW - thesaurus AU - Chen AU - Hsinchun AU - Schatz AU - Bruce, R AU - Yim AU - Tak AU - Fye AU - David PY - 1995/// UR - http://dlist.sir.arizona.edu/497/ ER -