An algorithmic approach to building concept space for a scientific community

Author

Chen, H.

Author_Institution

Dept. of MIS, Arizona Univ., Tucson, AZ, USA

Volume

4

fYear

1994

fDate

4-7 Jan. 1994

Firstpage

201

Lastpage

210

Abstract

This research reports an algorithmic approach to the generation of an organizational (community) memory for a scientific community. The techniques used included object filtering, automatic indexing, and cluster analysis. The testbed for our research was the Worm Community System, which contained various forms of (C. elegans) worm-related knowledge and literature. Currently in use by molecular biologists in the C. elegans-related research community. The resulting organizational memory was represented as a knowledge base (or frame-based thesaurus). It included 2,709 researchers´ names, 798 gene names, 20 experimental methods, and 4,302 subject descriptors.<>

Keywords

bibliographic systems; biology computing; factographic databases; indexing; information systems; knowledge representation; pattern recognition; thesauri; Worm Community System; algorithmic approach; automatic indexing; cluster analysis; concept space; experimental methods; frame-based thesaurus; gene names; knowledge base; molecular biologists; object filtering; organizational memory; researcher names; scientific community; subject descriptors; worm-related knowledge;

fLanguage

English

Publisher

ieee

Conference_Titel

System Sciences, 1994. Proceedings of the Twenty-Seventh Hawaii International Conference on

Conference_Location

Wailea, HI, USA

Print_ISBN

0-8186-5090-7

Type

conf

DOI

10.1109/HICSS.1994.323443

Filename

323443