Mapping of science by combined co-citation and word analysis. I. Structural aspects

Abstract

The claim that co-citation analysis is a useful tool to map subject-matter specialties of scientific research in a given period, is examined. A method has been developed using quantitative analysis of content-words related to publications in order to: (1) study coherence of research topics within sets of publications citing clusters, i.e., (part of) the "current work" of a specialty; (2) to study differences in research topics between sets of publications citing different clusters; and (3) to evaluate recall of "current work" publications concerning the specialties identified by co-citation analysis. Empirical support is found for the claim that co-citation analysis identifies indeed subject-matter specialties. However, different clusters may identify the same specialty, and results are far from complete concerning the identified "current work." These results are in accordance with the opinion of some experts in the fields. Low recall of co-citation analysis concerning the "current work" of specialties is shown to be related to the way in which researchers build their work on earlier publications: the "missed" publications equally build on very recent earlier work, but are less "consensual" and/or less "attentive" in their referencing practice. Evaluation of national research performance using co-citation analysis appears to be biased by this "incompleteness."

A new technique for building maps of large scientific domains based on the cocitation of classes and categories

“…[18][19][20][21][22][23][24][25] After the 90's, new methods of information retrieval and new techniques for the analysis, visualization and spatial positioning of information (well reviewed by Börner, Chen and Boyack 26 ), studies based on techniques for visualizing the structure of small scientific domains begin to proliferate. So, for instance, Braam, Moed and van Raan 27,28 propose the combined use of cocitation with co-word analysis for the generation of science maps, emphasizing their structure and dynamic aspects. Lin, Soergel and Marchionini 29 develop a Self-Organizing Map (SOM) that represents the semantic relationships among documents and can be used as a bibliographic interface for the retrieval of online information.…”

Intelligent scientific authoring tools: Interactive data mining for constructive uses of citation networks

“…Janssens et al (2008) gave an overview of studies published since the seminal work of Braam et al (1991), which have shown that hybrid clustering methods that incorporate text and citation information can outperform clustering methods that use only one of these components, and they proposed a new hybrid method that outperforms both. Another form of hybrid clustering combines text-based document similarities with author similarities; this can be used for example for expert finding (Tho et al, 2007).…”

“…We encourage interactive model construction and labelling (as in Fortuna et al, 2006), leverage citation and textual information (as in Braam et al, 1991;Chen, 2006), and build on word profiles for characterising the content of document clusters (as in Braam et al, 1991;Chen, 2006;Fortuna et al, 2006;Janssens et al, 2008). In addition, we extend these functionalities by helps for personal productivity (storage) and sharing with others.…”

“…Citation measures are derived from the citation matrix; text measures are derived from the abstract (a traditional choice, see e.g. Braam et al, 1991;Janssens et al, 2008). Text-based similarity is either based on words alone (optionally weighted by TF.IDF) or on LSA.…”

“…This is done to avoid arbitrary assignments while respecting that citation-based clusters do not represent the entire relevant literature that covers a topic (Braam et al, 1991). …”