This research reports an algorithmic approach to the automatic generation of thesauri for electronic community systems. The techniques used included term filtering, aufomatic 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. ekgans. 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. IntroductionElectronic community systems, which encode a research community's information and knowledge and provide an online environment to support the manipulation of that knowledge, have drawn significant attention recently due to the rapid proliferation and advancement of computing, databases, and telecommunication

An understanding of issues in the current health care environment arising from recent and proposed changes in Medicare rules and rates for pharmaceutical reimbursement can help position pharmacists to manage these issues.

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