Search (493 results, page 1 of 25)

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Abstract

Author research impact was examined based on citer analysis (the number of citers as opposed to the number of citations) for 90 highly cited authors grouped into three broad subject areas. Citer-based outcome measures were also compared with more traditional citation-based measures for levels of association. The authors found that there are significant differences in citer-based outcomes among the three broad subject areas examined and that there is a high degree of correlation between citer and citation-based measures for all measures compared, except for two outcomes calculated for the social sciences. Citer-based measures do produce slightly different rankings of authors based on citer counts when compared to more traditional citation counts. Examples are provided. Citation measures may not adequately address the influence, or reach, of an author because citations usually do not address the origin of the citation beyond self-citations.

Date

28. 9.2010 12:54:22

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Abstract

Effective patent management is essential for organizations to maintain their competitive advantage. The classification of patents is a critical part of patent management and industrial analysis. This study proposes a hybrid-patent-classification approach that combines a novel patent-network-based classification method with three conventional classification methods to analyze query patents and predict their classes. The novel patent network contains various types of nodes that represent different features extracted from patent documents. The nodes are connected based on the relationship metrics derived from the patent metadata. The proposed classification method predicts a query patent's class by analyzing all reachable nodes in the patent network and calculating their relevance to the query patent. It then classifies the query patent with a modified k-nearest neighbor classifier. To further improve the approach, we combine it with content-based, citation-based, and metadata-based classification methods to develop a hybrid-classification approach. We evaluate the performance of the hybrid approach on a test dataset of patent documents obtained from the U.S. Patent and Trademark Office, and compare its performance with that of the three conventional methods. The results demonstrate that the proposed patent-network-based approach yields more accurate class predictions than the patent network-based approach.

Date

22. 1.2011 13:04:21

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Abstract

Properties of a percentile-based rating scale needed in bibliometrics are formulated. Based on these properties, P100 was recently introduced as a new citation-rank approach (Bornmann, Leydesdorff, & Wang, 2013). In this paper, we conceptualize P100 and propose an improvement which we call P100'. Advantages and disadvantages of citation-rank indicators are noted.

Date

22. 8.2014 17:05:18

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Abstract

Compared to previous studies that generally detect scientific breakthroughs based on citation patterns, this article proposes a knowledge entity-based disruption indicator by quantifying the change of knowledge directly created and inspired by scientific breakthroughs to their evolutionary trajectories. Two groups of analytic units, including MeSH terms and their co-occurrences, are employed independently by the indicator to measure the change of knowledge. The effectiveness of the proposed indicators was evaluated against the four datasets of scientific breakthroughs derived from four recognition trials. In terms of identifying scientific breakthroughs, the proposed disruption indicator based on MeSH co-occurrences outperforms that based on MeSH terms and three earlier citation-based disruption indicators. It is also shown that in our indicator, measuring the change of knowledge inspired by the focal paper in its evolutionary trajectory is a larger contributor than measuring the change created by the focal paper. Our study not only offers empirical insights into conceptual understanding of scientific breakthroughs but also provides practical disruption indicator for scientists and science management agencies searching for valuable research.

Date

22. 1.2023 18:37:33

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Theme

Computer Based Training

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Abstract

A model based on a set of bibliometric indicators is proposed for the prediction of the ranking of applicants to an academic position as produced by a committee of peers. The results show that a very small number of indicators may lead to a robust prediction of about 75% of the cases. We start with 12 indicators to build a few composite indicators by factor analysis. Following a discrete choice model, we arrive at 3 comparatively good predicative models. We conclude that these models have a surprisingly good predictive power and may help peers in their selection process.

Date

18. 3.2014 18:22:21

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Abstract

The mapping of scientific fields, based on principles established in the seventies, has recently shown a remarkable development and applications are now booming with progress in computing efficiency. We examine here the convergence of two thematic mapping approaches, citation-based and word-based, which rely on quite different sociological backgrounds. A corpus in the nanoscience field was broken down into research themes, using the same clustering technique on the 2 networks separately. The tool for comparison is the table of intersections of the M clusters (here M=50) built on either side. A classical visual exploitation of such contingency tables is based on correspondence analysis. We investigate a rearrangement of the intersection table (block modeling), resulting in pseudo-map. The interest of this representation for confronting the two breakdowns is discussed. The amount of convergence found is, in our view, a strong argument in favor of the reliability of bibliometric mapping. However, the outcomes are not convergent at the degree where they can be substituted for each other. Differences highlight the complementarity between approaches based on different networks. In contrast with the strong informetric posture found in recent literature, where lexical and citation markers are considered as miscible tokens, the framework proposed here does not mix the two elements at an early stage, in compliance with their contrasted logic.

Date

8. 1.2011 18:22:50

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Abstract

Internet based electronic resources are growing dramatically but there have been no empirical studies evaluating the impact of e-sources, as a whole, on formal scholarly communication. reports results of an investigation into how much e-sources have been used in formal scholarly communication, using a case study in the area of Library and Information Science (LIS) during the period 1994 to 1996. 4 citation based indicators were used in the study of the impact measurement. Concludes that, compared with the impact of print sources, the impact of e-sources on formal scholarly communication in LIS is small, as measured by e-sources cited, and does not increase significantly by year even though there is observable growth of these impact across the years. It is found that periodical format is related to the rate of citing e-sources, articles are more likely to cite e-sources than are print priodical articles. However, once authors cite electronic resource, there is no significant difference in the number of references per article by periodical format or by year. Suggests that, at this stage, citing e-sources may depend on authors rather than the periodical format in which authors choose to publish

Date

30. 1.1999 17:22:22

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Abstract

Reports results of a bibliometric study of 111 theses accepted by the Department of Library and Information Science, Ahmadu Bello University, Zaria, Nigeria, between 1977 and 1992. The analysis was based on year, type and degree awarded, subject, type of library and geographical area. Concludes that the highest number of submissions was 1991, when 108 MLS theses (97,29%) and 3 PhD theses (2,71%) were accepted. Libraries and readers was the most concetrated subject while the academic library was the most discussed type of library

Source

Library focus. 13/14(1995/96), S.22-25

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Abstract

Contributions of DRTC to informetric studies and research are discussed. A report on recent work - a quantitative country-wise analysis of software literature based on the data from two bibliographic databases i.e. COMPENDEX and INSPEC is presented. The number of countries involved in R & D activities in software in the most productive group is increasing. The research contribution on software is decreasing in developed countries as compared to that in developing and less developed countries. India 's contribution is only 1.1% and it has remained constant over the period of 12 years 1989-2001. The number of countries involved in R&D activities in software has been increasing in the 1990s. It is also noted that higher the budget for higher education, higher the number of publications; and that higher the number of publications, higher the export as well as the domestic consumption of software.

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Abstract

We present a new, two-stage, self-supervised algorithm for author disambiguation in large bibliographic databases. In the first "bootstrap" stage, a collection of high-precision features is used to bootstrap a training set with positive and negative examples of coreferring authors. A supervised feature-based classifier is then trained on the bootstrap clusters and used to cluster the authors in a larger unlabeled dataset. Our self-supervised approach shares the advantages of unsupervised approaches (no need for expensive hand labels) as well as supervised approaches (a rich set of features that can be discriminatively trained). The algorithm disambiguates 54,000,000 author instances in Thomson Reuters' Web of Knowledge with B3 F1 of.807. We analyze parameters and features, particularly those from citation networks, which have not been deeply investigated in author disambiguation. The most important citation feature is self-citation, which can be approximated without expensive extraction of the full network. For the supervised stage, the minor improvement due to other citation features (increasing F1 from.748 to.767) suggests they may not be worth the trouble of extracting from databases that don't already have them. A lean feature set without expensive abstract and title features performs 130 times faster with about equal F1.

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Abstract

A theory of "megacitation" is introduced and used in an experiment to demonstrate how a qualitative scholarly book review can be converted into a weighted bibliometric indicator. We employ a manual human-coding approach to classify book reviews in the field of history based on reviewers' assessments of a book author's scholarly credibility (SC) and writing style (WS). In total, 100 book reviews were selected from the American Historical Review and coded for their positive/negative valence on these two dimensions. Most were coded as positive (68% for SC and 47% for WS), and there was also a small positive correlation between SC and WS (r = 0.2). We then constructed a classifier, combining both manual design and machine learning, to categorize sentiment-based sentences in history book reviews. The machine classifier produced a matched accuracy (matched to the human coding) of approximately 75% for SC and 64% for WS. WS was found to be more difficult to classify by machine than SC because of the reviewers' use of more subtle language. With further training data, a machine-learning approach could be useful for automatically classifying a large number of history book reviews at once. Weighted megacitations can be especially valuable if they are used in conjunction with regular book/journal citations, and "libcitations" (i.e., library holding counts) for a comprehensive assessment of a book/monograph's scholarly impact.

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Abstract

The consistent finding that internationally coauthored papers are more heavily cited has led to a tacit agreement among politicians and scientists that international collaboration in scientific research should be particularly promoted. However, existing studies of research collaboration suffer from a major weakness in that the Thomson Reuters Web of Science until recently did not link author names with affiliation addresses. The general approach has been to hierarchically code papers into international paper, national paper, or local paper based on the address information. This hierarchical coding scheme severely understates the level and contribution of local or national collaboration on an internationally coauthored paper. In this research, I code collaboration variables by hand checking each paper in the sample, use two measures of a paper's impact, and try several regression models. I find that both international collaboration and local collaboration are positively and significantly associated with a paper's impact, but international collaboration does not have more epistemic authority than local collaboration. This result suggests that previous findings based on hierarchical coding might be misleading.

Date

26. 9.2009 11:22:05

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Abstract

This study examines the differences between Scopus and Web of Science in the citation counting, citation ranking, and h-index of 22 top human-computer interaction (HCI) researchers from EQUATOR - a large British Interdisciplinary Research Collaboration project. Results indicate that Scopus provides significantly more coverage of HCI literature than Web of Science, primarily due to coverage of relevant ACM and IEEE peer-reviewed conference proceedings. No significant differences exist between the two databases if citations in journals only are compared. Although broader coverage of the literature does not significantly alter the relative citation ranking of individual researchers, Scopus helps distinguish between the researchers in a more nuanced fashion than Web of Science in both citation counting and h-index. Scopus also generates significantly different maps of citation networks of individual scholars than those generated by Web of Science. The study also presents a comparison of h-index scores based on Google Scholar with those based on the union of Scopus and Web of Science. The study concludes that Scopus can be used as a sole data source for citation-based research and evaluation in HCI, especially when citations in conference proceedings are sought, and that researchers should manually calculate h scores instead of relying on system calculations.

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Abstract

Webometric network analyses have been used to map the connectivity of groups of websites to identify clusters, important sites or overall structure. Such analyses have mainly been based upon hyperlink counts, the number of hyperlinks between a pair of websites, although some have used title mentions or URL citations instead. The ability to automatically gather hyperlink counts from Yahoo! ceased in April 2011 and the ability to manually gather such counts was due to cease by early 2012, creating a need for alternatives. This article assesses URL citations and title mentions as possible replacements for hyperlinks in both binary and weighted direct link and co-inlink network diagrams. It also assesses three different types of data for the network connections: hit count estimates, counts of matching URLs, and filtered counts of matching URLs. Results from analyses of U.S. library and information science departments and U.K. universities give evidence that metrics based upon URLs or titles can be appropriate replacements for metrics based upon hyperlinks for both binary and weighted networks, although filtered counts of matching URLs are necessary to give the best results for co-title mention and co-URL citation network diagrams.

Date

6. 4.2012 18:16:22

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Abstract

With the growth in biomedical literature, the necessity of extracting useful information from the literature has increased. One approach to extracting biomedical knowledge involves using citation relations to discover entity relations. The assumption is that citation relations between any two articles connect knowledge entities across the articles, enabling the detection of implicit relationships among biomedical entities. The goal of this article is to examine the characteristics of biomedical entities connected via intermediate entities using citation relations aided by text mining. Based on the importance of symptoms as biomedical entities, we created triples connected via citation relations to identify drug-disease pairs with shared symptoms as intermediate entities. Drug-disease interactions built via citation relations were compared with co-occurrence-based interactions. Several types of analyses were adopted to examine the properties of the extracted entity pairs by comparing them with drug-disease interaction databases. We attempted to identify the characteristics of drug-disease pairs through citation relations in association with biomedical entities. The results showed that the citation relation-based approach resulted in diverse types of biomedical entities and preserved topical consistency. In addition, drug-disease pairs identified only via citation relations are interesting for clinical trials when they are examined using BITOLA.

Date

1.11.2018 18:19:22

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Abstract

Citation analysis has long been used by librarians as an important tool of collection development and the advent of Internet technology and especially the WWW adds a new facet to the role played by citation analysis. One of the reasons why librarians create WWW homepages is to provide users with further sources of interest or reference and to do this libraries include links from their own homepages to other information sources. Reports current research on the analysis of WWW pages as an introduction to an examination of the homepages of 25 art libraries to determine what sites are most often included. The types of linked sites are analyzed based on 3 criteria: location, focus and evidence that the link was evaluated before the connection was establisheds

Date

10. 1.1999 16:22:37

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Abstract

This study uses three bibliometric methods: direct citation, bibliographic coupling, and co-authorship analysis, to investigate interdisciplinary changes in library and information science (LIS) from 1978 to 2007. The results reveal that LIS researchers most frequently cite publications in their own discipline. In addition, half of all co-authors of LIS articles are affiliated with LIS-related institutes. The results confirm that the degree of interdisciplinarity within LIS has increased, particularly co-authorship. However, the study found sources of direct citations in LIS articles are widely distributed across 30 disciplines, but co-authors of LIS articles are distributed across only 25 disciplines. The degree of interdisciplinarity was found ranging from 0.61 to 0.82 with citation to references in all articles being the highest and that of co-authorship being the lowest. Percentages of contribution attributable to LIS show a decreasing tendency based on the results of direct citation and co-authorship analysis, but an increasing tendency based on those of bibliographic coupling analysis. Such differences indicate each of the three bibliometric methods has its strength and provides insights respectively for viewing various aspects of interdisciplinarity, suggesting the use of no single bibliometric method can reveal all aspects of interdisciplinarity due to its multifaceted nature.

Source

Journal of the American Society for Information Science and Technology. 63(2012) no.1, S.22-33

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Abstract

A statistical analysis of full text downloads of articles in Elsevier's ScienceDirect covering all disciplines reveals large differences in download frequencies, their skewness, and their correlation with Scopus-based citation counts, between disciplines, journals, and document types. Download counts tend to be 2 orders of magnitude higher and less skewedly distributed than citations. A mathematical model based on the sum of two exponentials does not adequately capture monthly download counts. The degree of correlation at the article level within a journal is similar to that at the journal level in the discipline covered by that journal, suggesting that the differences between journals are, to a large extent, discipline specific. Despite the fact that in all studied journals download and citation counts per article positively correlate, little overlap may exist between the set of articles appearing in the top of the citation distribution and that with the most frequently downloaded ones. Usage and citation leaks, bulk downloading, differences between reader and author populations in a subject field, the type of document or its content, differences in obsolescence patterns between downloads and citations, and different functions of reading and citing in the research process all provide possible explanations of differences between download and citation distributions.

Date

22. 1.2016 14:11:17

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Abstract

Predicting a researcher's knowledge trajectories beyond their current foci can leverage potential inter-/cross-/multi-disciplinary interactions to achieve exploratory innovation. In this study, we present a method of diffusion-based network analytics for knowledge trajectory recommendation. The method begins by constructing a heterogeneous bibliometric network consisting of a co-topic layer and a co-authorship layer. A novel link prediction approach with a diffusion strategy is then used to capture the interactions between social elements (e.g., collaboration) and knowledge elements (e.g., technological similarity) in the process of exploratory innovation. This diffusion strategy differentiates the interactions occurring among homogeneous and heterogeneous nodes in the heterogeneous bibliometric network and weights the strengths of these interactions. Two sets of experiments-one with a local dataset and the other with a global dataset-demonstrate that the proposed method is prior to 10 selected baselines in link prediction, recommender systems, and upstream graph representation learning. A case study recommending knowledge trajectories of information scientists with topical hierarchy and explainable mediators reveals the proposed method's reliability and potential practical uses in broad scenarios.

Date

22. 6.2023 18:07:12