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Abstract

Science mapping is discussed in the general context of information visualization. Attempts to construct maps of science using citation data are reviewed, focusing on the use of co-citation clusters. New work is reported on a dataset of about 36.000 documents using simplified methods for ordination, and nesting maps hierarchically. an overall map of the dataset shows the multidisciplinary breadth of the document sample, and submaps allow drilling down the document level. An effort to visualize these data using advanced virtual reality software is described, and the creation of document pathways through the map is seen as a realization of Bush's associative trails

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Abstract

Historically, co-citation models have been based only on bibliographic information. Full-text analysis offers the opportunity to significantly improve the quality of the signals upon which these co-citation models are based. In this work we study the effect of reference proximity on the accuracy of co-citation clusters. Using a corpus of 270,521 full text documents from 2007, we compare the results of traditional co-citation clustering using only the bibliographic information to results from co-citation clustering where proximity between reference pairs is factored into the pairwise relationships. We find that accounting for reference proximity from full text can increase the textual coherence (a measure of accuracy) of a co-citation cluster solution by up to 30% over the traditional approach based on bibliographic information.