Extending the Visualization of Classification Interaction with Semantic Associations


  • Richard Smiraglia Knowledge Organization Research Group School of Information Studies University of Wisconsin, Milwaukee




General classification schemes hold the potential for being applied to large quantities of information resources. Yet the underlying infrastructure requires empirical understanding of the interaction between classifications and their inherent characteristics, as well as the inherent characteristics of the resources they classify. An important step is described here based on an attempt to derive terms from subject vocabularies (subject headings, index terms, terms from thesauri) in relation to UDC strings extracted from a random sample of KU Leuven MARC records and OCLC WorldCat MARC records. Results show see the clear presence of semantic clusters, which in future research might be generated from UDC strings and associated with other statistically-significant correlations to develop a navigable classificatory infrastructure for data-mining and information-sharing.