By Vladimir Geroimenko (auth.), Vladimir Geroimenko PhD, DSc, Chaomei Chen PhD (eds.)
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Additional resources for Visualizing the Semantic Web: XML-based Internet and Information Visualization
Scientists in general and information scientists in particular have been influenced by Thomas Kuhn's structure of scientific revolutions (Kuhn, 1962), Paul Thagard conceptual revolutions (Thagard, 1992), and Diana Crane's invisible colleges (Crane, 1972). The notion of tracking scientific paradigms originates in this influence. Two fruitful strands of efforts are particularly worth noting here. One is the work of Eugene Garfield and Henry Small at the Institute for Scientific Information (ISI) in mapping science through citation analysis.
The big green spheres represent ontology classes, with an attached label stating their name and cardinality. g. Ir is a subclass of Job Vacancies). Balloon-shaped edges connect objects to their most specific class(es). Objects with the same class membership are grouped in clusters. Our example contains six clusters; two of them represent overlaps between classes. The organization of the graph is computed using a variant of the well-known spring-embedder algorithm (Eades, 1984). On the one hand the class and cluster nodes repel each other.
4 shows a ranked list of documents in the first specialty. 932, whieh implies that Narin's article is the most representative of this specialty. In the Semantic Web, a bibliographie agent could go out and fetch further details of this article, such as its tide and abstract, behind the scenes. Furthermore, an automated summarization agent could produce a short list of keywords that can be used to characterize the nature of the specialty. 5 lists the predominant documents in the second specialty.