Artificial Intelligence Lab, Department of MIS, University of Arizona / Previous research in information science and in human-computer interaction has shown that people tend to use different terms to describe a similar concept. Due to the unique backgrounds, training, and experiences of different
people, the chance of two collaborators using the same term to describe a concept or an object for a common task is quite low. This vocabulary difference has created difficulties for both synchronous and asynchronous
collaborations. Bridging the gap between vocabularies of different collaborators is one of the most pressing challenges for computer-supported cooperative work (CSCW) system designers. In this research we propose a concept space approach and describe its associated algorithms for solving the vocabulary problem. For illustration
purposes, we present two implementation examples. The first implementation involved extracting and linking C. elegans worm-specific vocabularies for assisting molecular biology researchers in information retrieval and information sharing. The second example describes a system which helped resolve meeting participants' vocabulary problem during a group brainstorming and idea organization process. By adopting automatic indexing, cluster analysis, and neural network classification techniques, this research
has shown the feasibility of an algorithmic approach to solving the vocabulary problem in collaboration.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/106436 |
| Date | January 1993 |
| Creators | Chen, Hsinchun |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Conference Paper |
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