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Please use this identifier to cite or link to this item: http://hdl.handle.net/1860/1954

Title: Visualizing latent domain knowledge
Authors: Chen, Chaomei
Kuljis, Jasna
Paul, Ray J.
Keywords: Citation Chains;Knowledge Discovery;Knowledge Domain Visualization (KDViz);Latent Domain Knowledge
Issue Date: Nov-2001
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: IEEE Transactions, 31(4): pp.518-529.
Abstract: Knowledge discovery and data mining commonly rely on finding salient patterns of association from a vast amount of data. Traditional citation analysis of scientific literature draws insights from strong citation patterns. Latent domain knowledge, in contrast to the mainstream domain knowledge, often consists of highly relevant but relatively infrequently cited scientific works. Visualizing latent domain knowledge presents a significant challenge to knowledge discovery and quantitative studies of science. In this paper, we build upon a citation-based knowledge visualization procedure and develop an approach that not only captures knowledge structures from prominent and highly cited works, but also traces latent domain knowledge through low-frequency citation chains.We apply this approach to two cases: 1) identifying cross-domain applications of Pathfinder networks (PFNETs) and 2) clarifying the current status of scientific inquiry of a possible link between Bovine spongiform encephalopathy (BSE), also known as mad cow disease, and a new variant Creutzfeldt–Jakob disease (vCJD), a type of brain disease in human.
URI: http://dx.doi.org/10.1109/5326.983935
Appears in Collections:Faculty Research and Publications (IST)

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