Search (16 results, page 1 of 1)

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Abstract

The domain of knowledge organization (KO) represents a foundational area of information science. One way to better understand the intellectual structure of the KO domain is to apply bibliometric methods to key contributors to the literature. This study analyzes the most prolific contributing authors to the journal Knowledge Organization, the sources they cite and the citations they receive for the period 1993 to 2016. The analyses were conducted using visualization outcomes of citation, co-citation and author bibliographic coupling analysis to reveal theoretical points of reference among authors and the most prominent research themes that constitute this scientific community. Birger Hjørland was the most cited author, and was situated at or near the middle of each of the maps based on different citation relationships. The proximities between authors resulting from the different citation relationships demonstrate how authors situate themselves intellectually through the citations they give and how other authors situate them through the citations received. There is a consistent core of theoretical references as well among the most productive authors. We observed a close network of scholarly communication between the authors cited in this core, which indicates the actual role of the journal Knowledge Organization as a space for knowledge construction in the area of knowledge organization.

Source

Knowledge organization. 45(2018) no.1, S.13-22

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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

Zang and Wolfram compute the discrimination value for terms as the difference between the centroid value of all terms in the corpus and that value without the term in question, and suggest selection be made by comparing density changes with a visualization tool. The Distance Angle Retrieval Environment (DARE) visually projects a document or term space by presenting distance similarity on the X axis and angular similarity on the Y axis. Thus a document icon appearing close to the X axis would be relevant to reference points in terms of a distance similarity measure, while those close to the Y axis are relevant to reference points in terms of an angle based measure. Using 450 Associated Press news reports indexed by 44 distinct terms, the removal of the term ``Yeltsin'' causes the cluster to fall on the Y axis indicating a good discriminator. For an angular measure, cosine say, movement along the X axis to the left will signal good discrimination, as movement to the right will signal poor discrimination. A term density space could also be used. Most terms are shown to be indifferent discriminators. Different measures result in different choices as good and poor discriminators, as does the use of a term space rather than a document space. The visualization approach is clearly feasible, and provides some additional insights not found in the computation of a discrimination value.

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Abstract

Relationships between authors based on characteristics of published literature have been studied for decades. Author cocitation analysis using mapping techniques has been most frequently used to study how closely two authors are thought to be in intellectual space based on how members of the research community co-cite their works. Other approaches exist to study author relatedness based more directly on the text of their published works. In this study we present static and dynamic word-based approaches using vector space modeling, as well as a topic-based approach based on latent Dirichlet allocation for mapping author research relatedness. Vector space modeling is used to define an author space consisting of works by a given author. Outcomes for the two word-based approaches and a topic-based approach for 50 prolific authors in library and information science are compared with more traditional author cocitation analysis using multidimensional scaling and hierarchical cluster analysis. The two word-based approaches produced similar outcomes except where two authors were frequent co-authors for the majority of their articles. The topic-based approach produced the most distinctive map.

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Source

Journal of the American Society for Information Science. 46(1995) no.1, S.22-29

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Abstract

The authors investigate the frequency distribution of the use of image tags in Web pages. Using data sampled from top level Web pages across five top level domains and from sample pages within individual websites, the authors model observed patterns in the frequency of image tag usage by fitting collected data distributions to different theoretical models used in informetrics. Models tested include the modified power law (MPL), Mandelbrot (MDB), generalized waring (GW), generalized inverse Gaussian-Poisson (GIGP), and generalized negative binomial (GNB) distributions. The GIGP provided the best fit for data sets for top level pages across the top level domains tested. The poor fits of the models to the observed data distributions from specific websites were due to the multimodal nature of the observed data sets. Mixtures of the tested models for the data sets provided better fits. The ability to effectively model Web page attributes, such as the distribution of the number of image tags used per page, is needed for accurate simulation models of Web page content, and makes it possible to estimate the number of requests needed to display the complete content of Web pages.

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Abstract

Index modeling and computer simulation techniques are used to examine the influence of indexing frequency distributions, indexing exhaustivity distributions, and three weighting methods on hypothetical document spaces in a vector-based information retrieval (IR) system. The way documents are indexed plays an important role in retrieval. The authors demonstrate the influence of different indexing characteristics on document space density (DSD) changes and document space discriminative capacity for IR. Document environments that contain a relatively higher percentage of infrequently occurring terms provide lower density outcomes than do environments where a higher percentage of frequently occurring terms exists. Different indexing exhaustivity levels, however, have little influence on the document space densities. A weighting algorithm that favors higher weights for infrequently occurring terms results in the lowest overall document space densities, which allows documents to be more readily differentiated from one another. This in turn can positively influence IR. The authors also discuss the influence on outcomes using two methods of normalization of term weights (i.e., means and ranges) for the different weighting methods.

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Abstract

Examnies several feasibility issues and initial design considerations for a hypertext system for structured bibliographic retrieval. HyperLynx was developed using Asymmetric Toolbox. By combining searchable term indexes and inter-record linkages between common authors and descriptors, a flexible search environment is established conductive to browsing

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Abstract

Investigates how linkage environments in a hypertext based bibliographic retrieval system affect retrieval performance for novice and experienced searchers, 2 systems, 1 with inter record linkages to authors and descriptors and 1 that also included title and abstract keywords, were tested. No significant differences in retrieval performance and system usage were found for most search tests. The enhanced system did provide better performance where title and abstract keywords provided the most direct access to relevant records. The findings have implications for the design of bilbiographic information retrieval systems using hypertext linkages

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Abstract

A longstanding area of study in indexing is the identification of factors affecting vocabulary usage and consistency. This topic has seen a recent resurgence with a focus on social tagging. Tagging data for scholarly articles made available by the social bookmarking Website CiteULike (www.citeulike.org) were used to test the use of inter-indexer/tagger consistency density values, based on a method developed by the authors by comparing calculations for highly tagged documents representing three subject areas (Science, Social Science, Social Software). The analysis revealed that the developed method is viable for a large dataset. The findings also indicated that there were no significant differences in tagging consistency among the three topic areas, demonstrating that vocabulary usage in a relatively new subject area like social software is no more inconsistent than the more established subject areas investigated. The implications of the method used and the findings are discussed.

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Abstract

This paper examines the influence of the works of Hope A. Olson by conducting an ego-centric informetric analysis of her published works. Publication and citation data were collected from Google Scholar and the Thomson Reuters Web of Science. Classic informetrics techniques were applied to the datasets including co-authorship analysis, citer analysis, citation and co-citation analysis and text-based analysis. Co-citation and text-based data were analyzed and visualized using VOSviewer and CiteSpace, respectively. The analysis of her citation identity reveals how Dr. Olson situates her own research within the knowledge landscape while the analysis of her citation image reveals how others have situated her work in relation to the authors with whom she has been co-cited. This reflection of Dr. Olson's research contributions reveals the influence of her scholarship not only on knowledge organization but other areas of library and information science and allied disciplines.

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Abstract

The emergence of general audience digital libraries (GADLs) defines a context that represents a hybrid of both "traditional" IR, using primarily bibliographic resources provided by database vendors, and "popular" IR, exemplified by public search systems available on the World Wide Web. Findings of a study investigating end-user searching and response to a GADL are reported. Data collected from a Web-based end-user survey and data logs of resource usage for a Web-based GADL were analyzed for user characteristics, patterns of access and use, and user feedback. Cross-tabulations using respondent demographics revealed several key differences in how the system was used and valued by users of different age groups. Older users valued the service more than younger users and engaged in different searching and viewing behaviors. The GADL more closely resembles traditional retrieval systems in terms of content and purpose of use, but is more similar to popular IR systems in terms of user behavior and accessibility. A model that defines the dual context of the GADL environment is derived from the data analysis and existing IR models in general and other specific contexts. The authors demonstrate the distinguishing characteristics of this IR context, and discuss implications for the development and evaluation of future GADLs to accommodate a variety of user needs and expectations.

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Abstract

Purpose - The purpose of this article is to examine interindexer consistency on a larger scale than other studies have done to determine if group consensus is reached by larger numbers of indexers and what, if any, relationships emerge between assigned terms. Design/methodology/approach - In total, 64 MLIS students were recruited to assign up to five terms to a document. The authors applied basic data modeling and the exploratory statistical techniques of multi-dimensional scaling (MDS) and hierarchical cluster analysis to determine whether relationships exist in indexing consistency and the coocurrence of assigned terms. Findings - Consistency in the assignment of indexing terms to a document follows an inverse shape, although it is not strictly power law-based unlike many other social phenomena. The exploratory techniques revealed that groups of terms clustered together. The resulting term cooccurrence relationships were largely syntagmatic. Research limitations/implications - The results are based on the indexing of one article by non-expert indexers and are, thus, not generalizable. Based on the study findings, along with the growing popularity of folksonomies and the apparent authority of communally developed information resources, communally developed indexes based on group consensus may have merit. Originality/value - Consistency in the assignment of indexing terms has been studied primarily on a small scale. Few studies have examined indexing on a larger scale with more than a handful of indexers. Recognition of the differences in indexing assignment has implications for the development of public information systems, especially those that do not use a controlled vocabulary and those tagged by end-users. In such cases, multiple access points that accommodate the different ways that users interpret content are needed so that searchers may be guided to relevant content despite using different terminology.

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Abstract

The authors investigated 11 sports-related query keywords extracted from a public search engine query log to better understand sports-related information seeking on the Internet. After the query log contents were cleaned and query data were parsed, popular sports-related keywords were identified, along with frequently co-occurring query terms associated with the identified keywords. Relationships among each sports-related focus keyword and its related keywords were characterized and grouped using multidimensional scaling (MDS) in combination with traditional hierarchical clustering methods. The two approaches were synthesized in a visual context by highlighting the results of the hierarchical clustering analysis in the visual MDS configuration. Important events, people, subjects, merchandise, and so on related to a sport were illustrated, and relationships among the sports were analyzed. A small-scale comparative study of sports searches with and without term assistance was conducted. Searches that used search term assistance by relying on previous query term relationships outperformed the searches without the search term assistance. The findings of this study provide insights into sports information seeking behavior on the Internet. The developed method also may be applied to other query log subject areas.

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Abstract

Data citation, where products of research such as data sets, software, and tissue cultures are shared and acknowledged, is becoming more common in the era of Open Science. Currently, the practice of formal data citation-where data references are included alongside bibliographic references in the reference section of a publication-is uncommon. We examine the prevalence of data citation, documenting data sharing and reuse, in a sample of full text articles from the biological/biomedical sciences, the fields with the most public data sets available documented by the Data Citation Index (DCI). We develop a method that combines automated text extraction with human assessment for revealing candidate occurrences of data sharing and reuse by using terms that are most likely to indicate their occurrence. The analysis reveals that informal data citation in the main text of articles is far more common than formal data citations in the references of articles. As a result, data sharers do not receive documented credit for their data contributions in a similar way as authors do for their research articles because informal data citations are not recorded in sources such as the DCI. Ongoing challenges for the study of data citation are also outlined.