This research presents an algorithmic approach to addressing the vocabulary problem in scientific information retrieval and information sharing, using the molecular biology domain as an example. We first present a literature review of cognitive studies related to the vocabulary problem and vocabulary‐based search aids (thesauri) and then discuss techniques for building robust and domain‐specific thesauri to assist in cross‐domain scientific information retrieval. Using a variation of the automatic thesaurus generation techniques, which we refer to as the concept space approach, we recently conducted an experiment in the molecular biology domain in which we created a C. elegans worm thesaurus of 7,657 worm‐specific terms and a Drosophila fly thesaurus of 15,626 terms. About 30% of these terms overlapped, which created vocabulary paths from one subject domain to the other. Based on a cognitive study of term association involving four biologists, we found that a large percentage (59.6–85.6%) of the terms suggested by the subjects were identified in the conjoined fly‐worm thesaurus. However, we found only a small percentage (8.4–18.1%) of the associations suggested by the subjects in the thesaurus. In a follow‐up document retrieval study involving eight fly biologists, an actual worm database (Worm Community System), and the conjoined fly‐worm thesaurus, subjects were able to find more relevant documents (an increase from about 9 documents to 20) and to improve the document recall level (from 32.41 to 65.28%) when using the thesaurus, although the precision level did not improve significantly. Implications of adopting the concept space approach for addressing the vocabulary problem in internet and digital libraries applications are also discussed.

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In this article, we report research on an algorithmic approach to alleviating search uncertainty in a large information space. Grounded on object filtering, automatic indexing, and co‐occurrence analysis, we performed a large‐scale experiment using a parallel supercomputer (SGI Power Challenge) to analyze 400,000+ abstracts in an INSPEC computer engineering collection. Two system‐generated thesauri, one based on a combined object filtering and automatic indexing method, and the other based on automatic indexing only, were compared with the human‐generated INSPEC subject thesaurus. Our user evaluation revealed that the system‐generated thesauri were better than the INSPEC thesaurus in concept recall, but in concept precision the 3 thesauri were comparable. Our analysis also revealed that the terms suggested by the 3 thesauri were complementary and could be used to significantly increase “variety” in search terms and thereby reduce search uncertainty. © 1998 John Wiley & Sons, Inc.

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