Bibliometric factor maps for knowledge discovery in digital libraries
||Strotmann, Andreas; Dangzhi Zhao
||Bibliometric factor maps for knowledge discovery in digital libraries
||ELPUB2009. Rethinking Electronic Publishing: Innovation in Communication Paradigms and Technologies - Proceedings of the 13th International Conference on Electronic Publishing held in Milano, Italy 10-12 June 2009 / Edited by: Susanna Mornati and Turid Hedlund. ISBN 978-88-6134-326-6, 2009, pp. 501-512
||In this paper we describe the architecture of a visual bibliometric browsing plug-in for the growing number of digital libraries that provide cited references in their document meta-data, using a simple but effective visualization method for citation network analyses we recently introduced. Citation-based network analysis methods such as co-citation analysis have long been recognized as effective tools for gaining insight into the intellectual structure of a field through its literature. Visualizations of these networks can help the user get an intuitive aggregated overview of the field and the interrelationships between documents or authors, which in turn can aid query expansion, search refinement, and exploratory browsing. Our design calls for a visualization of the results of a multivariate factor analysis of a bibliometric similarity matrix calculated from a user's search results and/or from documents that are closely related to them. This provides the user a digital library with an interactive map of the literature that the user is interested in, where each visual element aggregates different aspects of the search result (authors and/or subfields). By helping the user see the forest for the trees (i.e., a structured visual landscape of the intellectual domain covered by the user's search and its bibliometric vicinity rather than a long list of search results), these maps and the relevant links they contain promise to provide a valuable aggregated browsing tool for digital libraries.
||bibliometric information retrieval, aggregated search, citation indexing, citation analysis, factor analysis
||file.pdf (920,983 bytes)
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