Search (7 results, page 1 of 1)

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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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Source

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