The proximity of co-citation

Traditional co-citation analysis has not taken the proximity of co-cited references into account. As long as two references are cited by the same article, they are retreated equally regardless the distance between where citations appear in the article. Little is known about what additional insights into citation and co-citation behaviours one might gain from studying distributions of co-citation in terms of such proximity. How are citations distributed in an article? What insights does the proximity of co-citation provide? In this article, the proximity of a pair of co-cited reference is defined as the nearest instance of the co-citation relation in text. We investigate the proximity of co-citation in full text of scientific publications at four levels, namely, the sentence level, the paragraph level, the section level, and the article level. We conducted four studies of co-citation patterns in the full text of articles published in 22 open access journals from BioMed Central. First, we compared the distributions of co-citation instances at four proximity levels in journal articles to the traditional article-level co-citation counts. Second, we studied the distributions of co-citations of various proximities across organizational sections in articles. Third, the distribution of co-citation proximity in different co-citation frequency groups is investigated. Fourth, we identified the occurrences of co-citations at different proximity levels with reference to the corresponding traditional co-citation network. The results show that (1) the majority of co-citations are loosely coupled at the article level, (2) a higher proportion of sentence-level co-citations is found in high co-citation frequencies than in low co-citation frequencies, (3) tightly coupled sentence-level co-citations not only preserve the essential structure of the corresponding traditional co-citation network but also form a much smaller subset of the entire co-citation instances typically considered by traditional co-citation analysis. Implications for improving our understanding of underlying factors concerning co-citations and developing more efficient co-citation analysis methods are discussed.     

Sleeping beauties with no prince based on the co-citation criterion

In science, a sleeping beauty (SB) refers to a highly cited paper that initially remains unnoticed for several years after publication. It is generally recognized that each SB is awoken by princes, in the form of other papers that cite the SB. Investigations have shown that one feature of a prince is that it has a considerable number of co-citations with the SB. In the present study, we applied a method to screen for princes from among papers citing the SB based on the number of co-citations in the Web of Science and identified four SBs that were awoken without a prince based on the co-citation criterion. There are two possible explanations for this phenomenon. The first is that some SBs may not need a prince to attract the attention of other academics. The authors of three of the four abovementioned SBs were Nobel laureates, and their work was well known in their respective fields. The papers had slept for a long period because their work was ahead of its time. The long period of sleep is a reflection of the authors’ high academic level and degree of insight. The other potential explanation is that princes do not have to be co-cited with SBs. Our findings demonstrate a significant deficiency in the previous criteria relating to princes based on co-citations. Thus, new criteria are required to identify princes.

     

Evaluations of context-based co-citation searching

Since machine-readable documents have become widespread, some recent studies have proposed retrieval methods using a combination of citation linkage and its context. In the case of co-citation linkage, there have been attempts to discern ‘strong’ co-citations from ‘weak’ ones by examining the positions of citations in a document. However, this promising concept has not yet been sufficiently evaluated, and it remains unclear whether search performance is significantly improved. Therefore, this paper explores the effects of using co-citation context more deeply and more widely by comparing the search performance of six retrieval methods, which differ as to whether co-citation context and normalization using cited frequency are used. For empirically evaluating the effects, a special test collection was created from CiteSeer Metadata, and the search performances of the six retrieval methods were compared by two IR metrics (AP and nDCG). The main conclusions of this paper are: (1) co-citation context has a positive effect on co-citation searching; (2) the normalization technique using cited frequency is useful for context-based co-citation searching; (3) approaches of using co-citation context tend to affect the characteristics of search performance.     

Related records retrieval and pennant retrieval: an exploratory case study

The Related Records feature in the Web of Science retrieves records that share at least one item in their reference lists with the references of a seed record. This search method, known as bibliographic coupling, does not always yield topically relevant results. Our exploratory case study asks: How do retrievals of the type used in pennant diagrams compare with retrievals through Related Records? Pennants are two-dimensional visualizations of documents co-cited with a seed paper. In them, the well-known tf*idf (term frequency*inverse document frequency) formula is used to weight the co-citation counts. The weights have psychological interpretations from relevance theory; given the seed, tf predicts a co-cited document’s cognitive effects on the user, and idf predicts the user’s relative ease in relating its title to the seed’s title. We chose two seed papers from information science, one with only two references and the other with 20, and used them to retrieve 50 documents per method in WoS for each of our two seeds. We illustrate with pennant diagrams. Pennant retrieval indeed produced more relevant documents, especially for the paper with only two references, and it produced mostly different ones. Related Records performed almost as well on the paper with the longer reference list, improving remarkably as the coupling units between the seed and other papers increased. We argue that relevance rankings based on co-citation, with pennant-style weighting as an option, would be a desirable addition to WoS and similar databases.

     

Visualizing the structure and bridges of the intellectual property management and strategy literature: a document co-citation analysis

This article uses document co-citation analysis to objectively explore the underlying structure of the intellectual property research domain, taken from a managerial and strategic standpoint. The goal of this study is identifying its main research areas, understanding its current state of development and suggesting potential future directions, by analyzing the co-citations from 181 papers published between 1992 and 2011 in the most influential academic journals. Five main clusters have been identified, mapped, and labeled as follows: Economics of patent system, technological and institutional capabilities, university patenting, intellectual property exploitation, and division of labor. Their most active areas on this topic, and the most influential and co-cited papers have been identified and described. Also, intra- and inter-cluster knowledge base diversity has been assessed by using indicators stemming from the domains of information theory and biology. A t test has been performed to assess the significance of the inter-cluster diversity. The knowledge bases of these five clusters are significantly diverse, this meaning that they are five co-existing paradigms.     

A comparison of two bibliometric methods for mapping of the research front

Summary This paper builds on previous research concerned with the classification and specialty mapping of research fields. Two methods are put to test in order to decide if significant differences as to mapping results of the research front of a science field occur when compared. The first method was based on document co-citation analysis where papers citing co-citation clusters were assumed to reflect the research front. The second method was bibliographic coupling where likewise citing papers were assumed to reflect the research front. The application of these methods resulted in two different types of aggregations of papers: (1) groups of papers citing clusters of co-cited works and (2) clusters of bibliographically coupled papers. The comparision of the two methods as to mapping results was pursued by matching word profiles of groups of papers citing a particular co-citation cluster with word profiles of clusters of bibliographically coupled papers. Findings suggested that the research front was portrayed in two considerably different ways by the methods applied. It was concluded that the results in this study would support a further comparative study of these methods on a more detailed and qualitative ground. The original data set encompassed 73,379 articles from the fifty most cited environmental science journals listed in Journal Citation Report, science edition downloaded from the Science Citation Index on CD-ROM.     

The interdisciplinary structure of research on intercultural relations: a co-citation network analysis study

This study aims to map the content and structure of the knowledge base of research on intercultural relations as revealed in co-citation networks of 30 years of scholarly publications. Source records for extracting co-citation information are retrieved from Web of Science (1980–2010) through comprehensive keyword search and filtered by manual semantic coding. Exploratory network and content analysis is conducted (1) to discover the development of major research themes and the relations between them over time; (2) to locate representative core publications (the stars) that are highly co-cited with others and those (the bridges) connecting more between rather than within subfields or disciplines. Structural analysis of the co-citation networks identifies a core cluster that contains foundational knowledge of this domain. It is well connected to almost all the other clusters and covers a wide range of subject categories. The evolutionary path of research themes shows trends moving towards (e.g. psychology and business and economics) and away from (e.g. language education and communication) the core cluster over time. Based on the results, a structural framework of the knowledge domain of intercultural relations research is proposed to represent thematic relatedness between topical groups and their relations.     

Journal co-citation analysis of semiconductor literature

The purpose of this study is to map semiconductor literature using journal co-citation analysis. The journal sample was gathered from the INSPEC database from 1978 to 1997. In the co-citation analysis, the data compiled were counts of the number of times two journal titles were jointly cited in later publications. It is assumed that the more two journals are cited together, the closer the relationship between them. The journal set used was the 30 most productive journals in the field of semiconductors. Counts of co-citations to the set of semiconductor journals were retrieved from SciSearch database, accessed through Dialog. Cluster analysis and multi-dimensional scaling were employed to create two-dimensional maps of journal relationships in the cross-citation networks. The following results were obtained through this co-citation study: The 30 journals fall fairly clearly into three clusters. The major cluster of journals, containing 17 titles, is in the subject of physics. The second cluster, consisting of 9 journals, includes journals primarily on material science. The remaining cluster represents research areas in the discipline of electrical and electronic engineering. All co-cited journals share similar co-citation profiles, reflected in high positive Pearson correlation. Two hundred and ninety-six pairs (68%) correlate at greater than 0.70. This shows that there is strong relationship between semiconductor journals. Five individual journals in five paired sets with co-citation frequency over 100,000 times include Physical Review B, Condensed Matter; Physical Review Letters; Applied Physics Letters; Journal of Applied Physics; and Solid State Communications. This revised version was published online in August 2006 with corrections to the Cover Date.     

The cognitive structure of current cardiovascular research

This paper presents a citation analysis of the cognitive structure of current cardiovascularresearch. Used methods are co-citation analysis, bibliographic coupling and quantitative analysisof title words. Tables and graphs reveal: (1) The journal co-citation structure; (2) the cognitivecontent and the bibliometric structure of clusters based on co-citation; (3) the cognitive contentand the bibliometric structure of clusters based on bibliographic coupling. A predominance ofdifferent research aspects on coronary artery disease was found in clusters based on co-citations aswell as in clusters based on bibliographic coupling.     

Author co-citation analysis of semiconductor literature

The purpose of this study is to map semiconductor literature by author co-citation analysis in order to highlight major subject specializations in semiconductors and identify authors and their relationships within these specialties and within the field. Forty-six of the most productive authors were included in the sample list. Author samples were gathered from the INSPEC database from 1978 to 1997. The relatively low author co-citation frequencies indicate that there is a low connection among authors who publish in semiconductor journals and big differences among authors' research areas. Six sets of authors with co-citation greater than 100 times are M. Cardona and G. Lucovsky; T. Ito and K. Kobayashi; M. Cardona and G. Abstreiter; A. Y. Cho and H. Morkoc; C. R. Abernathy and W. S. Hobson; H. Morkoc and I. Akasaki. The Pearson correlation coefficient of author co-citation varies widely, i.e., from -0.17 to 0.92. This shows that some authors with high positive correlations are related in certain ways and co-cited, while other authors with high negative correlations may be rarely or never related and co-cited. Cluster analysis and multi-dimensional scaling are employed to create two-dimensional maps of author relationships in the cross-citation networks. It is found that the authors fall fairly clearly into three clusters. The first cluster covers authors in physics and its applications. The authors in the second group are experts in electrical and electronic engineering. The third group includes specialists in materials science. Because of its interdisciplinary nature and diverse subjects, semiconductor literature lacks a strong group of core authors. The field consists of several specialties around a weak center. This revised version was published online in June 2006 with corrections to the Cover Date.     

Exploring the modelling and simulation knowledge base through journal co-citation analysis

Co-citation analysis is a form of content analysis that can be applied in the context of scholarly publications with the purpose of identifying prominent articles, authors and journals being referenced to by the citing authors. It identifies co-cited references that occur in the reference list of two or more citing articles, with the resultant co-citation network providing insights into the constituents of a knowledge domain (e.g., significant authors and papers). The contribution of the paper is twofold; (a) the demonstration of the added value of using co-citation analysis, and for this purpose the underlying dataset that is chosen is the peer-reviewed publication of the Society for Modeling and Simulation International (SCS)—SIMULATION; (b) the year 2012 being the 60th anniversary of the SCS, the authors hope that this paper will lead to further acknowledgement and appreciation of the Society in charting the growth of Modeling and Simulation (M&S) as a discipline.     

Interpreting maps of science using citation context sentiments: a preliminary investigation

It is proposed that citation contexts, the text surrounding references in scientific papers, be analyzed in terms of an expanded notion of sentiment, defined to include attitudes and dispositions toward the cited work. Maps of science at both the specialty and global levels are used as the basis of this analysis. Citation context samples are taken at these levels and contrasted for the appearance of cue word sets, analyzed with the aid of methods from corpus linguistics. Sentiments are shown to vary within a specialty and can be understood in terms of cognitive and social factors. Within-specialty and between-specialty co-citations are contrasted and in some cases suggest a correlation of sentiment with structural location. For example, the sentiment of “uncertainty” is important in interdisciplinary co-citation links, while “utility” is more prevalent within the specialty. Suggestions are made for linking sentiments to technical terms, and for developing sentiment “baselines” for all of science.     

A dense network sub-grouping algorithm for co-citation analysis and its implementation in the software tool Sitkis

Summary Clustering algorithms are used prominently in co-citation analysis by analysts aiming to reveal research streams within a field. However, clustering of widely cited articles is not robust to small variations in citation patterns. We propose an alternative algorithm, dense network sub-grouping, which identifies dense groups of co-cited references. We demonstrate the algorithm using a data set from the field of family business research and compare it to two alternative methods, multidimensional scaling and clustering. We also introduce a free software tool, Sitkis, that implements the algorithm and other common bibliometric methods. The software identifies journal-, country- and university-specific citation patterns and co-citation groups, enabling the identification of “invisible colleges.”     

Citation analysis of research performer quality

Background: Citation analysis for evaluative purposes typically requires normalization against some control group of similar papers. Selection of this control group is an open question. Objectives: Gain a better understanding of control group requirements for credible normalization. Approach: Performed citation analysis on prior publications of two proposing research units, to help estimate team research quality. Compared citations of each unit"s publications to citations received by thematically and temporally similar papers. Results: Identification of thematically similar papers was very complex and labor intensive, even with relatively few control papers selected. Conclusions: A credible citation analysis for determining performer or team quality should have the following components: – Multiple technical experts to average out individual bias and subjectivity; – A process for comparing performer or team output papers with a normalization base of similar papers; – A process for retrieving a substantial fraction of candidate normalization base papers; Manual evaluation of many candidate normalization base papers to obtain high thematic similarity and statistical representation.     

逆向学习耦合多属性查询的图像排序/检索优化算法研究

目的提出逆向学习耦合多属性查询的图像同步排序/检索优化算法,以解决当前算法检索效率与精度不高等问题。方法引入逆向学习概念,利用复杂无损函数,设计图像检索机制,优化训练误差。考虑查询项属性相关性,将训练图像分割成多个子集,联合权重因子,构造图像排序模型。对于给定的多属性查询,文中算法可以利用查询项中隐含的单词属性完成检索。结果文中算法支持多标记查询,与当前图像排序搜索机制相比,在多属性查询条件下,文中算法具有更高的检索精度(当查全率为80%时,精度较对照组分别提高了8.3%和13.2%)与效率。结论文中算法能够支持多属性查询,能进一步增加检索精度。     

Tracking and predicting growth areas in science

Summary We explore the possibility of using co-citation clusters over three time periods to track the emergence and growth of research areas, and predict their near term change. Data sets are from three overlapping six-year periods: 1996-2001, 1997-2002 and 1998-2003. The methodologies of co-citation clustering, mapping, and string formation are reviewed, and a measure of cluster currency is defined as the average age of highly cited papers relative to the year span of the data set. An association is found between the currency variable in a prior period and the percentage change in cluster size and citation frequency in the following period. The conflating factor of “single-issue clusters” is discussed and dealt with using a new metric called in-group citation.     

Using neural-network based paragraph embeddings for the calculation of within and between document similarities

Science mapping using document networks comes often with the implicit assumption that scientific papers are indivisible units with unique links to neighbour documents. Research on proximity in co-citation analysis and the study of lexical properties of sections and citation contexts indicate that this assumption doesn’t always hold. Moreover, the meaning of words and co-words depends on the context in which they appear. This study proposes the use of a neural network architecture for word and paragraph embeddings (Doc2Vec) for the measurement of similarity among those smaller units of analysis. It is shown that paragraphs in the “Introduction” and the “Discussion” Section are more similar to the abstract, that the similarity among paragraphs is related to -but not linearly- the distance between the paragraphs. The “Methodology” Section is least similar to the other sections. Abstracts of citing-cited documents are more similar than random pairs and the context in which a reference appears is most similar to the abstract of the cited document. This novel approach with higher granularity can be used for bibliometric aided retrieval and to assist in measuring interdisciplinarity through the application of network-based centrality measures.

     

A relevance ranking method for citation-based search results

A growing number of researchers are exploring the use of citation relationships such as direct citation, bibliographic coupling, and co-citation for information retrieval in scientific databases and digital libraries. In this paper, I propose a method of ranking the relevance of citation-based search results to a set of key, or seed, papers by measuring the number of citation relationships they share with those key papers. I tested the method against 23 published systematic reviews and found that the method retrieved 87% of the studies included in these reviews. The relevance ranking approach identified a subset of the citation search results that comprised 27% of the total documents retrieved by the method, and 7% of the documents retrieved by these reviews, but that contained 75% of the studies included in these reviews. Additional testing suggested that the method may be less appropriate for reviews that combine literature in ways that are not reflected in the literature itself. These results suggest that this ranking method could be useful in a range of information retrieval contexts.     

Visualizing the research on pervasive and ubiquitous computing

The aim of this paper is to identify the research status quo on pervasive and ubiquitous computing via scientometric analysis. Information visualization and knowledge domain visualization techniques were adopted to determine how the study of pervasive and ubiquitous computing has evolved. A total of 5,914 papers published between 1995 and 2009 were retrieved from the Web of Science with a topic search of pervasive or ubiquitous computing. CiteSpace is a java application for analyzing and visualizing a wide range of networks from bibliographic data. By use of it, we generated the subject category network to identify the leading research fields, the research power network to find out the most productive countries and institutes, the journal co-citation map to identify the distribution of core journals, the author co-citation map to identify key scholars and their co-citation patterns, the document co-citation network to reveal the ground-breaking literature and detect the co-citation clusters on pervasive and ubiquitous computing, and depicted the hybrid network of keywords and noun phrases to explore research foci on pervasive and ubiquitous computing over the entire span 1995–2009.     

Digital competences,computer skills and information literacy in secondary education: mapping and visualization of trends and concepts

Digital competences, computer skills, information literacy and related abilities represent a crucial element in ICT education (Information and Communication Technologies). They are less frequently investigated in the frame of secondary education than in higher education. We assess these contexts in secondary education through science mapping and visualizing techniques, examining publishing patterns and trends. Databases Web of Science (WOS) and Scopus are used. Publishing exhibits logarithmic inverse relationship between rank and frequency (power laws). Only a few chief publications among hundreds account for an important share of all published research. WOS-based visualizations (VOSviewer software) of concepts used in titles, abstracts and keywords show several clusters of research: computer-, information-, as well as digital-related. Further analysis reveals that the major terms which define these clusters predominate in different periods. Computer-related are earlier terms, followed by information-related, and now digital-related. As some concepts mature terminology embraces more trendy novel concepts. Clusters of co-citing and co-cited sources shows differences among publications. Proceedings play an important role as sources of co-citations but are cited more weakly. Both co-citing and co-cited sources exhibit well defined clusters with little convergence between Library and Information Science on one side and Education and Educational Research, and Computer Science on the other even though the respective publications employ similar terminological concepts. The lack of exchange between these research domains calls for more co-operation in order to boost synergy in these critical twenty-first century skills.