Search (8 results, page 1 of 1)

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Abstract

Until recently, Chinese texts could not be studied using co-word analysis because the words are not separated by spaces in Chinese (and Japanese). A word can be composed of one or more characters. The online availability of programs that separate Chinese texts makes it possible to analyze them using semantic maps. Chinese characters contain not only information but also meaning. This may enhance the readability of semantic maps. In this study, we analyze 58 words which occur 10 or more times in the 1,652 journal titles of the China Scientific and Technical Papers and Citations Database. The word-occurrence matrix is visualized and factor-analyzed.

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Abstract

The dynamic analysis of structural change in the organization of the sciences requires, methodologically, the integration of multivariate and time-series analysis. Structural change - for instance, interdisciplinary development - is often an objective of government interventions. Recent developments in multidimensional scaling (MDS) enable us to distinguish the stress originating in each time-slice from the stress originating from the sequencing of time-slices, and thus to locally optimize the trade-offs between these two sources of variance in the animation. Furthermore, visualization programs like Pajek and Visone allow us to show not only the positions of the nodes, but also their relational attributes such as betweenness centrality. Betweenness centrality in the vector space can be considered as an indicator of interdisciplinarity. Using this indicator, the dynamics of the citation-impact environments of the journals Cognitive Science, Social Networks, and Nanotechnology are animated and assessed in terms of interdisciplinarity among the disciplines involved.

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Abstract

Co-occurrence matrices, such as cocitation, coword, and colink matrices, have been used widely in the information sciences. However, confusion and controversy have hindered the proper statistical analysis of these data. The underlying problem, in our opinion, involved understanding the nature of various types of matrices. This article discusses the difference between a symmetrical cocitation matrix and an asymmetrical citation matrix as well as the appropriate statistical techniques that can be applied to each of these matrices, respectively. Similarity measures (such as the Pearson correlation coefficient or the cosine) should not be applied to the symmetrical cocitation matrix but can be applied to the asymmetrical citation matrix to derive the proximity matrix. The argument is illustrated with examples. The study then extends the application of co-occurrence matrices to the Web environment, in which the nature of the available data and thus data collection methods are different from those of traditional databases such as the Science Citation Index. A set of data collected with the Google Scholar search engine is analyzed by using both the traditional methods of multivariate analysis and the new visualization software Pajek, which is based on social network analysis and graph theory.

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Abstract

Via the Internet, information scientists can obtain costfree access to large databases in the "hidden" or "deep Web." These databases are often structured far more than the Internet domains themselves. The patent database of the U.S. Patent and Trade Office is used in this study to examine the science base of patents in terms of the literature references in these patents. Universitybased patents at the global level are compared with results when using the national economy of the Netherlands as a system of reference. Methods for accessing the online databases and for the visualization of the results are specified. The conclusion is that "biotechnology" has historically generated a model for theorizing about university-industry relations that cannot easily be generalized to other sectors and disciplines.

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Abstract

The debate about which similarity measure one should use for the normalization in the case of Author Co-citation Analysis (ACA) is further complicated when one distinguishes between the symmetrical co-citation - or, more generally, co-occurrence - matrix and the underlying asymmetrical citation - occurrence - matrix. In the Web environment, the approach of retrieving original citation data is often not feasible. In that case, one should use the Jaccard index, but preferentially after adding the number of total citations (i.e., occurrences) on the main diagonal. Unlike Salton's cosine and the Pearson correlation, the Jaccard index abstracts from the shape of the distributions and focuses only on the intersection and the sum of the two sets. Since the correlations in the co-occurrence matrix may be spurious, this property of the Jaccard index can be considered as an advantage in this case.

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Date

22. 5.2003 19:48:04

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Date

6.11.2005 19:02:22

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Date

22. 3.2009 19:07:20