MAPPING “CARTOGRAPHY” BASED ON THE CONTENT OF PEER-REVIEWED
JOURNAL ARTICLES
A. Skupin, W. Pang
skupin@mail.sdsu.edu
Knowledge domain visualization is a recently emerged approach to the challenge of making sense of the volume of intellectual output generated across the sciences. It aims at giving users visual access to the content and structures of the documents and communities through which different sciences communicate their intellectual progress. In this quest, the structure and development of knowledge domains, such as biology or anthropology, are typically analyzed based on network-type information extracted from documents and respective metadata. For example, citation networks can be derived based on papers citing other papers. Such networks are thought to reflect the transfer and transformation of knowledge within a domain. Similar processes are captured by authorship networks, which are based on overlapping co-authorship of documents. Quantitative analysis of citation networks is the basis of numerical measures of the impact of different documents and authors and several examples of visual display of these networks likewise exist. Such analysis is almost always based on large citation databases, especially those produced by the Institute for Scientific Information (ISI), such as the database underlying the Web of Science.
In this paper it is demonstrated that
instead of focusing solely on network
structures one could also visualize a set of documents based on text content derived from a bibliometric
database. Specifically, the experiment described here involves first performing
a keyword search for “cartography” within a database containing several million
journal articles. Then, through a series of computational transformations the
retrieved records are transformed into a spatialization of the corresponding
document space. The result is a visualization reflecting semantic structures
among those documents in the database within which the term “cartography”
appears. The discussion contrasts this with an earlier visualization, described
by Skupin and de Jongh at the previous