Search (10 results, page 1 of 1)

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Abstract

In an earlier study by the authors, full-text analysis and traditional bibliometric methods were combined to map research papers published in the journal Scientometrics. The main objective was to develop appropriate techniques of full-text analysis and to improve the efficiency of the individual methods in the mapping of science. The number of papers was, however, rather limited. In the present study, we extend the quantitative linguistic part of the previous studies to a set of five journals representing the field of Library and Information Science (LIS). Almost 1000 articles and notes published in the period 2002-2004 have been selected for this exercise. The optimum solution for clustering LIS is found for six clusters. The combination of different mapping techniques, applied to the full text of scientific publications, results in a characteristic tripod pattern. Besides two clusters in bibliometrics, one cluster in information retrieval and one containing general issues, webometrics and patent studies are identified as small but emerging clusters within LIS. The study is concluded with the analysis of cluster representations by the selected journals.

Field

Bibliothekswesen
Informationswissenschaft

Source

Information processing and management. 42(2006) no.6, S.1614-1642

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Series

Schriften zur Informationswissenschaft; Bd.16

Source

Mehrwert von Information - Professionalisierung der Informationsarbeit: Proceedings des 4. Internationalen Symposiums für Informationswissenschaft (ISI'94), Graz, 2.-4. November 1994. Hrsg.: W. Rauch u.a

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Footnote

Beitrag in einem Special Issue "Combining bibliometrics and information retrieval"

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Abstract

In this article, the authors answer the question of whether the field of scientometrics/bibliometrics shares essential characteristics of "metrics" sciences. To achieve this objective, the citation network of seven selected metrics and their information environment is analyzed.

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.7, S.1515-1518

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Abstract

This article offers important background information about a new product, the Book Citation Index (BKCI), launched in 2011 by Thomson Reuters. Information is illustrated by some new facts concerning The BKCI's use in bibliometrics, coverage analysis, and a series of idiosyncrasies worthy of further discussion. The BKCI was launched primarily to assist researchers identify useful and relevant research that was previously invisible to them, owing to the lack of significant book content in citation indexes such as the Web of Science. So far, the content of 33,000 books has been added to the desktops of the global research community, the majority in the arts, humanities, and social sciences fields. Initial analyses of the data from The BKCI have indicated that The BKCI, in its current version, should not be used for bibliometric or evaluative purposes. The most significant limitations to this potential application are the high share of publications without address information, the inflation of publication counts, the lack of cumulative citation counts from different hierarchical levels, and inconsistency in citation counts between the cited reference search and the book citation index. However, The BKCI is a first step toward creating a reliable and necessary citation data source for monographs - a very challenging issue, because, unlike journals and conference proceedings, books have specific requirements, and several problems emerge not only in the context of subject classification, but also in their role as cited publications and in citing publications.

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.7, S.1388-1398

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Abstract

We propose a new hybrid clustering framework to incorporate text mining with bibliometrics in journal set analysis. The framework integrates two different approaches: clustering ensemble and kernel-fusion clustering. To improve the flexibility and the efficiency of processing large-scale data, we propose an information-based weighting scheme to leverage the effect of multiple data sources in hybrid clustering. Three different algorithms are extended by the proposed weighting scheme and they are employed on a large journal set retrieved from the Web of Science (WoS) database. The clustering performance of the proposed algorithms is systematically evaluated using multiple evaluation methods, and they were cross-compared with alternative methods. Experimental results demonstrate that the proposed weighted hybrid clustering strategy is superior to other methods in clustering performance and efficiency. The proposed approach also provides a more refined structural mapping of journal sets, which is useful for monitoring and detecting new trends in different scientific fields.

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1105-1119

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Source

Journal of the American Society for Information Science and Technology. 62(2011) no.7, S.1403-1411

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Source

Journal of the American Society for Information Science and Technology. 63(2012) no.7, S.1420-1425

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Footnote

Artikel in einem Themenheft "Current theory in library and information science"

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Source

Journal of the Association for Information Science and Technology. 67(2016) no.5, S.1257-1265