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Abstract

Bibliographies and their knowledge organization systems commonly cover broad topical areas. Indexes to knowledge organization systems, such as the Subject Index to the Dewey Decimal Classification, provide a general index to the entirety. However, every community and every specialty develops its own specialized vocabulary. An index derived from the specialized use of language within a single subdomain could well be different from a general-purpose index for all domains and preferable for that subdomain. Statistical association techniques can be used to create indexes to knowledge systems. A preliminary analysis based on the INSPEC database shows that subdomain indexes differ significantly from each other and from a general index. The greater the polysemy of individual words the greater difference in the indexes

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Abstract

The emerging network environment brings access to an increasing population of heterogeneous repositories. Inevitably, these, have quite diverse metadata vocabularies (categorization codes, classification numbers, index and thesaurus terms). So, necessarily, the number of metadata vocabularies that are accessible but unfamiliar for any individual searcher is increasing steeply. When an unfamiliar metadata vocabulary is encountered, how is a searcher to know which codes or terms will lead to what is wanted? This paper reports work at the University of California, Berkeley, on the design and development of English language indexes to metadata vocabularies. Further details and the current status of the work can be found at the project website http://www.sims.berkeley.edu/research/metadata/