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In this chapter, we have included two case studies. Our visualizations have shown
the potential of the citation-based approach to knowledge discovery and to tracking
scientific paradigms. We do not expect that such visualizations would replace review
articles and surveys carefully made by domain experts. Instead, such visualizations,
if done properly, may lead to a more sensible literature search methodology than
the current fashionable but somewhat piecemeal retrieval-oriented approaches. By
taking into account values perceived by those who have domain expertise, our
generic approach has shown the potential of such visualizations as an alternative
“camera” to take snapshots of scientific frontiers.
We have drawn a great deal of valuable background information from Kormendy
and Richstone's article Inward Bound (Kormendy and Richstone 1995 ). It was this
article that dominated the visualization landscape of the latest period. Kuhn later
suggested that specialization was more common. Instead of killing off a traditional
rival line of research immediately, a new branch of research may run in parallel.
The search for supermassive black hole is rapidly advancing. The media is full
of news on latest discoveries. In fact, the latest news announced at the winter
2001 American Astronomical Society meeting suggested that HST and the Chandra
X-ray Observatory have found evidence for an event horizon on Cygnus X-1, the
first object identified as a black hole candidate. Scientific visualism is increasingly
finding its way in modern science.
There are several possible research avenues to further develop this generic
approach to visualizing competing paradigms, for example:
1. Apply this approach to classic paradigm shifts identified by Kuhn and others
2. Refine the philosophical and sociological foundations of this approach.
3. Combine citation analysis with other modeling and analysis techniques, such as
automatic citation context indexing and latent semantic indexing (LSI), so as to
provide a more balance view of scientific frontiers.
4. Extend the scope of applications to a wider range of disciplines.
5. Track the development of the two case studies in the future with follow-up
6. Track the development of scientific frontiers. Work closely with domain experts
to evaluate and improve science mapping.
In the next chapter, we continue to explore issues concerning mapping scientific
frontiers with special focus on the discovery of latent domain knowledge. How do
scientists detect new and significant developments in knowledge? What does it take
a visualization metaphor to capture and predict the growth of knowledge? How do
we match the visualized intellectual structure to what scientists have in their mind?
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