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Visualizing latent domain knowledge
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|Title: ||Visualizing latent domain knowledge|
|Authors: ||Chen, Chaomei|
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.|
|Appears in Collections:||Faculty Research and Publications (IST)|
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