Visualizing latent domain knowledge

Chaomei Chen, Jasna Kuljis, Ray J. Paul

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)518-529
Number of pages12
JournalIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Issue number4
Publication statusPublished - 2001 Nov

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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