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1.
The general aim of this paper is to show the results of a study in which we combined bibliometric mapping and citation network analysis to investigate the process of creation and transfer of knowledge through scientific publications. The novelty of this approach is the combination of both methods. In this case we analyzed the citations to a very influential paper published in 1990 that contains, for the first time, the term Absorptive Capacity. A bibliometric map identified the terms and the theories associated with the term while two techniques from the citation network analysis recognized the main papers during 15 years. As a result we identified the articles that influenced the research for some time and linked them into a research tradition that can be considered the backbone of the “Absorptive Capacity Field”. 相似文献
2.
Document clustering of scientific texts using citation contexts 总被引：3，自引：0，他引：3
Document clustering has many important applications in the area of data mining and information retrieval. Many existing document
clustering techniques use the “bag-of-words” model to represent the content of a document. However, this representation is
only effective for grouping related documents when these documents share a large proportion of lexically equivalent terms.
In other words, instances of synonymy between related documents are ignored, which can reduce the effectiveness of applications
using a standard full-text document representation. To address this problem, we present a new approach for clustering scientific
documents, based on the utilization of citation contexts. A citation context is essentially the text surrounding the reference
markers used to refer to other scientific works. We hypothesize that citation contexts will provide relevant synonymous and
related vocabulary which will help increase the effectiveness of the bag-of-words representation. In this paper, we investigate
the power of these citation-specific word features, and compare them with the original document’s textual representation in
a document clustering task on two collections of labeled scientific journal papers from two distinct domains: High Energy
Physics and Genomics. We also compare these text-based clustering techniques with a link-based clustering algorithm which
determines the similarity between documents based on the number of co-citations, that is in-links represented by citing documents
and out-links represented by cited documents. Our experimental results indicate that the use of citation contexts, when combined
with the vocabulary in the full-text of the document, is a promising alternative means of capturing critical topics covered
by journal articles. More specifically, this document representation strategy when used by the clustering algorithm investigated
in this paper, outperforms both the full-text clustering approach and the link-based clustering technique on both scientific
journal datasets. 相似文献
3.
Yi Zhang Jie Lu Feng Liu Qian Liu Alan Porter Hongshu Chen Guangquan Zhang 《Journal of Informetrics》2018,12(4):1099-1117
Topic extraction presents challenges for the bibliometric community, and its performance still depends on human intervention and its practical areas. This paper proposes a novel kernel k-means clustering method incorporated with a word embedding model to create a solution that effectively extracts topics from bibliometric data. The experimental results of a comparison of this method with four clustering baselines (i.e., k-means, fuzzy c-means, principal component analysis, and topic models) on two bibliometric datasets demonstrate its effectiveness across either a relatively broad range of disciplines or a given domain. An empirical study on bibliometric topic extraction from articles published by three top-tier bibliometric journals between 2000 and 2017, supported by expert knowledge-based evaluations, provides supplemental evidence of the method’s ability on topic extraction. Additionally, this empirical analysis reveals insights into both overlapping and diverse research interests among the three journals that would benefit journal publishers, editorial boards, and research communities. 相似文献
4.
《Journal of Informetrics》2014,8(3):449-477
This paper analyzes several well-known bibliometric indices using an axiomatic approach. We concentrate on indices aiming at capturing the global impact of a scientific output and do not investigate indices aiming at capturing an average impact. Hence, the indices that we study are designed to evaluate authors or groups of authors but not journals. The bibliometric indices that are studied include classic ones such as the number of highly cited papers as well as more recent ones such as the h-index and the g-index. We give conditions that characterize these indices, up to the multiplication by a positive constant. We also study the bibliometric rankings that are induced by these indices. Hence, we provide a general framework for the comparison of bibliometric rankings and indices. 相似文献
5.
在分析现有文献计量软件的优缺点及利用文献计量方法进行科学研究的目的与工作流程的基础上,建立多种文献数据库题录字典,有效进行关键词的合并和修正,集成文献计量中统计、共词和聚类过程,设计和实现一种可视化的共词聚类分析系统。 相似文献
6.
Yu Zhang Min Wang Florian Gottwalt Morteza Saberi Elizabeth Chang 《Journal of Informetrics》2019,13(2):616-634
As the volume of scientific articles has grown rapidly over the last decades, evaluating their impact becomes critical for tracing valuable and significant research output. Many studies have proposed various ranking methods to estimate the prestige of academic papers using bibliometric methods. However, the weight of the links in bibliometric networks has been rarely considered for article ranking in existing literature. Such incomplete investigation in bibliometric methods could lead to biased ranking results. Therefore, a novel scientific article ranking algorithm, W-Rank, is introduced in this study proposing a weighting scheme. The scheme assigns weight to the links of citation network and authorship network by measuring citation relevance and author contribution. Combining the weighted bibliometric networks and a propagation algorithm, W-Rank is able to obtain article ranking results that are more reasonable than existing PageRank-based methods. Experiments are conducted on both arXiv hep-th and Microsoft Academic Graph datasets to verify the W-Rank and compare it with three renowned article ranking algorithms. Experimental results prove that the proposed weighting scheme assists the W-Rank in obtaining ranking results of higher accuracy and, in certain perspectives, outperforming the other algorithms. 相似文献
7.
A systematic empirical comparison of different approaches for normalizing citation impact indicators
We address the question how citation-based bibliometric indicators can best be normalized to ensure fair comparisons between publications from different scientific fields and different years. In a systematic large-scale empirical analysis, we compare a traditional normalization approach based on a field classification system with three source normalization approaches. We pay special attention to the selection of the publications included in the analysis. Publications in national scientific journals, popular scientific magazines, and trade magazines are not included. Unlike earlier studies, we use algorithmically constructed classification systems to evaluate the different normalization approaches. Our analysis shows that a source normalization approach based on the recently introduced idea of fractional citation counting does not perform well. Two other source normalization approaches generally outperform the classification-system-based normalization approach that we study. Our analysis therefore offers considerable support for the use of source-normalized bibliometric indicators. 相似文献
8.
[目的/意义]作者共引网络分析（ACNA）是文献计量学中的重要分析方法,旨在通过寻找学术文献集合中作者之间的共引关系绘制出特定领域的知识图谱,进而指导科学研究。然而,ACNA的一个缺陷是其原始矩阵输入信息量过小。本文通过提出作者混合共引网络（HACNA）,绘制更为精确的科学知识图谱。[方法/过程]鉴于不同种类的学术网络能为绘制知识图谱提供不同维度的信息,提高知识图谱绘制的精确性,本文以合著网络和引用网络为例,结合其他种类的学术网络在ACNA基础上进行精确科学知识图谱的绘制。[结果/结论]实证研究结果显示,与ACNA相比,HACNA绘制出的知识图谱在聚类过程中能够使得同类作者更为聚拢、不同类作者更为分散,从而提高了聚类效果和可视化程度。同时,HACNA绘制出的知识图谱还能够挖掘出更多细节。 相似文献
9.
《Journal of Informetrics》2014,8(2):369-383
Subject classification arises as an important topic for bibliometrics and scientometrics, searching to develop reliable and consistent tools and outputs. Such objectives also call for a well delimited underlying subject classification scheme that adequately reflects scientific fields. Within the broad ensemble of classification techniques, clustering analysis is one of the most successful.Two clustering algorithms based on modularity – the VOS and Louvain methods – are presented here for the purpose of updating and optimizing the journal classification of the SCImago Journal & Country Rank (SJR) platform. We used network analysis and Pajek visualization software to run both algorithms on a network of more than 18,000 SJR journals combining three citation-based measures of direct citation, co-citation and bibliographic coupling. The set of clusters obtained was termed through category labels assigned to SJR journals and significant words from journal titles.Despite the fact that both algorithms exhibited slight differences in performance, the results show a similar behaviour in grouping journals. Consequently, they are deemed to be appropriate solutions for classification purposes. The two newly generated algorithm-based classifications were compared to other bibliometric classification systems, including the original SJR and WoS Subject Categories, in order to validate their consistency, adequacy and accuracy. In addition to some noteworthy differences, we found a certain coherence and homogeneity among the four classification systems analysed. 相似文献
10.
This paper addresses emerging trends in the collective dynamics found in knowledge networks, those networks composed of the relationships among knowledge sources, such as citation networks and keyword networks. In studying the formation and detection of new trends in the process of knowledge evolution, we use the collective dynamics approach to construct a network of knowledge clusters based on citation clustering. This approach explores the processes and rules of new trends emerging in knowledge clusters by examining the continuous changes in keyword vectors found in the interaction and coordination between evolving knowledge clusters. In direct citation networks, the collective dynamics approach is found to be superior to the baseline method, especially in predicting small knowledge fields with less data and more uncertainties. 相似文献
11.
指出文献计量作为一种有效的评价手段,在生物医药领域,主要应用于学术期刊评价和科研绩效评价;传统的文献计量评价方法存在一些固有局限性,为此人们已作出许多创新和改进。分析讨论评价学术期刊的新模型和指标--渐进曲线模型和特征因子以及评价科研绩效的两种方法创新--多指标综合分析和基于社会网络的分析,并论述文献计量与经济社会因素的结合使用。从这些新型方法和指标的出现和应用可以看出,文献计量评价的发展呈现出借助数学模型和计算机手段,由单指标向多指标转换,结合复杂的社会网络特征和经济社会因素进行分析的大趋势。 相似文献
12.
Recent advances in methods and techniques enable us to develop interactive overlays to a global map of science based on aggregated citation relations among the 9162 journals contained in the Science Citation Index and Social Science Citation Index 2009. We first discuss the pros and cons of the various options: cited versus citing, multidimensional scaling versus spring-embedded algorithms, VOSViewer versus Gephi, and the various clustering algorithms and similarity criteria. Our approach focuses on the positions of journals in the multidimensional space spanned by the aggregated journal–journal citations. Using VOSViewer for the resulting mapping, a number of choices can be left to the user; we provide default options reflecting our preferences. Some examples are also provided; for example, the potential of using this technique to assess the interdisciplinarity of organizations and/or document sets. 相似文献
13.
《Journal of Informetrics》2022,16(2):101260
Understanding the reasons associated with successful proposals are of paramount importance to improve evaluation processes. In this context, we analyzed whether bibliometric features are able to predict the success of research grants. We extracted features aiming at characterizing the academic history of Brazilian researchers, including research topics, affiliations, number of publications and visibility. The extracted features were then used to predict grants productivity via machine learning in three major research areas, namely Medicine, Dentistry and Veterinary Medicine. We found that research subject and publication history play a role in predicting productivity. In addition, institution-based features turned out to be relevant when combined with other features. While the best results outperformed text-based attributes, the evaluated features were not highly discriminative. Our findings indicate that predicting grants success, at least with the considered set of bibliometric features, is not a trivial task. 相似文献
14.
论文对目前常用的两种文献计量可视化工具Cite Space和Hist Cite的功能进行比较分析。通过设计八个文献计量学指标作为两种工具的实验分析指标,以SCI中2003-2012年电子政务主题的1388篇研究性文献为实验文献样本,对这两种工具在事先设定好的八个文献计量学指标中的功能表现进行分析。最终从具体分析功能、图谱可读性和支持的数据格式三个角度对这两个工具的功能进行了对比。 相似文献
15.
In economics the Research Papers in Economics (RePEc) network has become an essential source for the gathering and the spread of both existing and new economic research. Furthermore, it is currently the largest bibliometric database in economic sciences containing 33 different indicators for more than 30,000 economists. Based on this bibliographic information RePEc calculates well-known rankings for authors and academic institutions. We provide some cautionary remarks concerning the interpretation of some provided bibliometric measures in RePEc. Moreover, we show how individual and aggregated rankings can be biased due to the employed ranking methodology. In order to select key indicators describing and assessing research performance of scientist, we propose to apply principal component analysis in this data-rich environment. This approach allows us to assign weights to each indicator prior to aggregation. We illustrate the approach by providing a new overall ranking of economists based on RePEc data. 相似文献
16.
This paper explores the use of Library Catalog Analysis (LCA), defined as the application of bibliometric or informetric techniques to a set of library online catalogs, to describe quantitatively a scientific-scholarly field on the basis of published book titles. It focuses on its value as a tool in studies of Social Sciences and Humanities, especially its cognitive structures, main book publishers and the research performance of its actors. The paper proposes an analogy model between traditional citation analysis of journal articles and Library Catalog Analysis of book titles. It presents the outcomes of an exploratory study of book titles in Economics included in 42 academic library catalogs from 7 countries. It describes the process of data collection and cleaning, and applies a series of indicators and thematic mapping techniques. It illustrates how LCA can be fruitfully used to assess book production and research performance at the level of an individual researcher, a research department, an entire country and a book publisher. It discusses a number of issues that should be addressed in follow-up studies and concludes that LCA of published book titles can be developed into a powerful and useful tool in studies of Social Sciences and Humanities. 相似文献
17.
The purpose of this paper is to analyse and describe the topological properties of the institutional and national collaboration network from the profiles extracted from Google Scholar Citations (GSC). 19,912 unique profiles with “co-authors” were obtained from a web crawl performed in March 2012. Several statistical and network analysis techniques were used to map and analyse these collaboration relationships at the country and institution level. Results show that The United States dominates the world scientific map and that every research institution is grouped by national, geographical and cultural criteria. A clustering phenomenon based on the self-similarity and fractal properties of scale-free networks is also observed. We conclude that GSC is a suitable tool for collaboration studies only at macro level between countries and institutions. 相似文献
18.
《Journal of Informetrics》2023,17(3):101426
Risk plays a fundamental role in scientific discoveries, and thus it is critical that the level of risk can be systematically quantified. We propose a novel approach to measuring risk entailed in a particular mode of discovery process – knowledge recombination. The recombination of extant knowledge serves as an important route to generate new knowledge, but attempts of recombination often fail. Drawing on machine learning and natural language processing techniques, our approach converts knowledge elements in the text format into high-dimensional vector expressions and computes the probability of failing to combine a pair of knowledge elements. Testing the calculated risk indicator on survey data, we confirm that our indicator is correlated with self-assessed risk. Further, as risk and novelty have been confounded in the literature, we examine and suggest the divergence of the bibliometric novelty and risk indicators. Finally, we demonstrate that our risk indicator is negatively associated with future citation impact, suggesting that risk-taking itself may not necessarily pay off. Our approach can assist decision making of scientists and relevant parties such as policymakers, funding bodies, and R&D managers. 相似文献
19.
Livia Iacovino 《Archival Science》2004,4(3-4):267-286
This article describes the issues involved in using a multi-method approach to address multi-faceted interdisciplinary research
in archival science. The example chosen to illustrate the multi-method approach is taken from recent research, which explored
the recordkeeping-ethics-law nexus from the perspective of communities as social systems, regulatory models for recordkeeping
and their continuing application to online records. The methods combined traditional archival and social science research
techniques, as well as legal and ethics research tools drawn from law and moral philosophy, together with disciplinary discourse
analysis, concept mapping and empirical examples to illustrate the concepts. The example demonstrates that complex research
questions that cross disciplinary boundaries need to draw from a number of research paradigms and conceptual understandings,
which assist in breaking down the barriers with knowledge domains that have to date, had limited contact with archival science. 相似文献
20.
Evaluative bibliometrics is concerned with comparing research units by using statistical procedures. According to Williams (2012) an empirical study should be concerned with the substantive and practical significance of the findings as well as the sign and statistical significance of effects. In this study we will explain what adjusted predictions and marginal effects are and how useful they are for institutional evaluative bibliometrics. As an illustration, we will calculate a regression model using publications (and citation data) produced by four universities in German-speaking countries from 1980 to 2010. We will show how these predictions and effects can be estimated and plotted, and how this makes it far easier to get a practical feel for the substantive meaning of results in evaluative bibliometric studies. An added benefit of this approach is that it makes it far easier to explain results obtained via sophisticated statistical techniques to a broader and sometimes non-technical audience. We will focus particularly on Average Adjusted Predictions (AAPs), Average Marginal Effects (AMEs), Adjusted Predictions at Representative Values (APRVs) and Marginal Effects at Representative Values (MERVs). 相似文献