The Dynamics of Scientific Knowledge - Mapping Scientific Frontiers: The Quest for Knowledge Visualization - page 44

Chapter 3 , “ Mapping Associations ”, extends the spatial metaphors described
in Chap. 2 to capture the essence of conceptual worlds. On the one hand, we
distinguish the uniqueness of mapping conceptual systems. On the other hand,
it is our intention to consolidate design strategies and visual representations that
can be carried through into the new realm. This chapter introduces some of the
most commonly used methods to generate visual-spatial models of concepts and
their interrelationships. Examples in this chapter demonstrate not only the use
of classic multivariate analysis methods such as multidimensional scaling (MDS)
and principle component analysis (PCA), but also the promising route for further
advances in non-linear multidimensional scaling. We introduce a number of network
modeling and analysis approaches.
Chapter 4 , “ Trajectories of Search ”, describes three interrelated aspects of
science mapping: structural modeling, visual-semantic displays, and behavioral
semantics. Structural mapping is concerned with how to extract meaningful rela-
tionships from information resources. Visual-semantic displays focus on the design
of effective channels for effective communication. Traditionally, structural mapping
and visual-semantic display are regarded as the core of information visualization.
Behavioral semantics emphasizes the meaning of behavioral patterns in helping us
to understand the structure of an information space. It also provides a promising
way to build responsive virtual environments. We expect these enabling techniques
will play an increasingly important role in mapping scientific frontiers.
Chapter 5 , “ The Structure and Dynamics of Scientific Knowledge ”, presents a
historical account of theories and quantitative methods of mapping science. Two
major streams of work, co-word analysis and co-citation analysis, are illustrated
with examples. The influence of information visualization is highlighted.
Chapter 6 , “Tracing Competing Paradigms ”, focuses on the visualization of
competing paradigms by using theories and techniques described in previous
chapters. This chapter demonstrates the process of detecting competing paradigms
through two detailed case studies. One is on the prolonged scientific debates among
geologists and paleontologists on mass extinctions. The other is on the search for
supermassive black holes and the active nuclei paradigm concerned by astronomers
and astrophysics.
Chapter 7 , “ Tracking the Latent Domain Knowledge ”, demonstrates three more
case studies on the theme of visualizing the dynamics of scientific frontiers. In
contrast to Chap. 6 , the case studies in this chapter emphasize the role of citation
networks in revealing less frequently cited works. The goal is to foster further
research in discovering paradigms.
Chapter 8 , “Mapping Science”, introduces a structural variation model to
measure the value of newly available information by conceptualizing the devel-
opment of scientific knowledge as a complex adaptive system. This chapter also
includes a case study of identifying emerging trends in regenerative medicine and a
study of retracted articles and their impacts on the literature. Global science maps
and interactive overlays are also introduced in this chapter. A new dual-map overlay
design is proposed to make citations explicit in terms of both source and target
journals of citation links.