Search (574 results, page 1 of 29)

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Abstract

A simpler model is proposed for estimating the frequency of any same-frequency words and identifying the boundary point between high-frequency words and low-frequency words in a text. The model, based on a 'maximum-ranking method', assigns ranks to the words and estimates word frequency by a formula. The boundary value between high-frequency and low-frequency words is obtained by taking the square root of the number of different words in the text. This straightforward model was used successfully with both English and Chinese texts

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Abstract

In this article, we build models to predict the existence of citations among papers by formulating link prediction for 5 large-scale datasets of citation networks. The supervised machine-learning model is applied with 11 features. As a result, our learner performs very well, with the F1 values of between 0.74 and 0.82. Three features in particular, link-based Jaccard coefficient difference in betweenness centrality, and cosine similarity of term frequency-inverse document frequency vectors, largely affect the predictions of citations. The results also indicate that different models are required for different types of research areas-research fields with a single issue or research fields with multiple issues. In the case of research fields with multiple issues, there are barriers among research fields because our results indicate that papers tend to be cited in each research field locally. Therefore, one must consider the typology of targeted research areas when building models for link prediction in citation networks.

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Abstract

Reports results of a bibliometric study of the literature of Qigong: a relaxation technique used to teach patients to control their heart rate, blood pressure, temperature and other involuntary functions through controlles breathing. All articles indexed in the MEDLINE CD-ROM database, between 1965 and 1995 were identified using 'breathing exercises' MeSH term. The articles were analyzed for geographical and language distribution and a ranking exercise enabled a core list of periodicals to be identified. In addition, the study shed light on the changing frequency of the MeSH terms and evaluated the research areas by measuring the information from these respective MeSH headings

Source

International forum on information and documentation. 22(1997) no.3, S.38-44

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Abstract

Online searchers today no longer limit themselves to locating references to articles. More and more, they are called upon to locate specific answers to questions such as: Who is my chief competitor for this technology? Who is publishing the most on this subject? What is the geographic distribution of this product? These questions demand answers, not necessarily from record content, but from statistical analysis of the terms in a set of records. Most online services now provide a tool for statistical analysis such as GET on Orbit, ZOOM on ESA/IRS and RANK/RANK FILES on Dialog. With these commands, users can analyze term frequency to extrapolate very precise answers to a wide range of questions. This paper discusses the many uses of term frequency analysis and how it can be applied to areas of competitive intelligence, market analysis, bibliometric analysis and improvements of search results. The applications are illustrated by examples from Dialog

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Abstract

Journals in the Information Science & Library Science category of Journal Citation Reports (JCR) were compared using both bibliometric and bibliographic features. Data collected covered journal impact factor (JIF), number of issues per year, number of authors per article, longevity, editorial board membership, frequency of publication, number of databases indexing the journal, number of aggregators providing full-text access, country of publication, JCR categories, Dewey decimal classification, and journal statement of scope. Three features significantly correlated with JIF: number of editorial board members and number of JCR categories in which a journal is listed correlated positively; journal longevity correlated negatively with JIF. Coword analysis of journal descriptions provided a proximity clustering of journals, which differed considerably from the clusters based on editorial board membership. Finally, a multiple linear regression model was built to predict the JIF based on all the collected bibliographic features.

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Abstract

One of the solutions to help scientists filter the most relevant publications and, thus, to stay current on developments in their fields during the transition from "little science" to "big science", was the introduction of citation indexing as a Wellsian "World Brain" (Garfield, 1964) of scientific information: It is too much to expect a research worker to spend an inordinate amount of time searching for the bibliographic descendants of antecedent papers. It would not be excessive to demand that the thorough scholar check all papers that have cited or criticized such papers, if they could be located quickly. The citation index makes this check practicable (Garfield, 1955, p. 108). In retrospective, citation indexing can be perceived as a pre-social web version of crowdsourcing, as it is based on the concept that the community of citing authors outperforms indexers in highlighting cognitive links between papers, particularly on the level of specific ideas and concepts (Garfield, 1983). Over the last 50 years, citation analysis and more generally, bibliometric methods, have developed from information retrieval tools to research evaluation metrics, where they are presumed to make scientific funding more efficient and effective (Moed, 2006). However, the dominance of bibliometric indicators in research evaluation has also led to significant goal displacement (Merton, 1957) and the oversimplification of notions of "research productivity" and "scientific quality", creating adverse effects such as salami publishing, honorary authorships, citation cartels, and misuse of indicators (Binswanger, 2015; Cronin and Sugimoto, 2014; Frey and Osterloh, 2006; Haustein and Larivière, 2015; Weingart, 2005).
Furthermore, the rise of the web, and subsequently, the social web, has challenged the quasi-monopolistic status of the journal as the main form of scholarly communication and citation indices as the primary assessment mechanisms. Scientific communication is becoming more open, transparent, and diverse: publications are increasingly open access; manuscripts, presentations, code, and data are shared online; research ideas and results are discussed and criticized openly on blogs; and new peer review experiments, with open post publication assessment by anonymous or non-anonymous referees, are underway. The diversification of scholarly production and assessment, paired with the increasing speed of the communication process, leads to an increased information overload (Bawden and Robinson, 2008), demanding new filters. The concept of altmetrics, short for alternative (to citation) metrics, was created out of an attempt to provide a filter (Priem et al., 2010) and to steer against the oversimplification of the measurement of scientific success solely on the basis of number of journal articles published and citations received, by considering a wider range of research outputs and metrics (Piwowar, 2013). Although the term altmetrics was introduced in a tweet in 2010 (Priem, 2010), the idea of capturing traces - "polymorphous mentioning" (Cronin et al., 1998, p. 1320) - of scholars and their documents on the web to measure "impact" of science in a broader manner than citations was introduced years before, largely in the context of webometrics (Almind and Ingwersen, 1997; Thelwall et al., 2005):
There will soon be a critical mass of web-based digital objects and usage statistics on which to model scholars' communication behaviors - publishing, posting, blogging, scanning, reading, downloading, glossing, linking, citing, recommending, acknowledging - and with which to track their scholarly influence and impact, broadly conceived and broadly felt (Cronin, 2005, p. 196). A decade after Cronin's prediction and five years after the coining of altmetrics, the time seems ripe to reflect upon the role of social media in scholarly communication. This Special Issue does so by providing an overview of current research on the indicators and metrics grouped under the umbrella term of altmetrics, on their relationships with traditional indicators of scientific activity, and on the uses that are made of the various social media platforms - on which these indicators are based - by scientists of various disciplines.

Date

20. 1.2015 18:30:22

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Abstract

We present PubSearch, a hybrid heuristic scheme for re-ranking academic papers retrieved from standard digital libraries such as the ACM Portal. The scheme is based on the hierarchical combination of a custom implementation of the term frequency heuristic, a time-depreciated citation score and a graph-theoretic computed score that relates the paper's index terms with each other. We designed and developed a meta-search engine that submits user queries to standard digital repositories of academic publications and re-ranks the repository results using the hierarchical heuristic scheme. We evaluate our proposed re-ranking scheme via user feedback against the results of ACM Portal on a total of 58 different user queries specified from 15 different users. The results show that our proposed scheme significantly outperforms ACM Portal in terms of retrieval precision as measured by most common metrics in Information Retrieval including Normalized Discounted Cumulative Gain (NDCG), Expected Reciprocal Rank (ERR) as well as a newly introduced lexicographic rule (LEX) of ranking search results. In particular, PubSearch outperforms ACM Portal by more than 77% in terms of ERR, by more than 11% in terms of NDCG, and by more than 907.5% in terms of LEX. We also re-rank the top-10 results of a subset of the original 58 user queries produced by Google Scholar, Microsoft Academic Search, and ArnetMiner; the results show that PubSearch compares very well against these search engines as well. The proposed scheme can be easily plugged in any existing search engine for retrieval of academic publications.

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Abstract

Fractional frequency distributions of, for example, authors with a certain (fractional) number of papers are very irregular and, therefore, not easy to model or to explain. This article gives a first attempt to this by assuming two simple Lotka laws (with exponent 2): one for the number of authors with n papers (total count here) and one for the number of papers with n authors, n E N. Based an an earlier made convolution model of Egghe, interpreted and reworked now for discrete scores, we are able to produce theoretical fractional frequency distributions with only one parameter, which are in very close agreement with the practical ones as found in a large dataset produced earlier by Rao. The article also shows that (irregular) fractional frequency distributions are a consequence of Lotka's law, and are not examples of breakdowns of this famous historical law.

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Abstract

A citation analysis of undergraduate term papers in microeconomics revealed a significant decrease in the frequency of scholarly resources cited between 1996 and 1999. Book citations decreased from 30% to 19%, newspaper citations increased from 7% to 19%, and Web citations increased from 9% to 21%. Web citations checked in 2000 revealed that only 18% of URLs cited in 1996 led to the correct Internet document. For 1999 bibliographies, only 55% of URLs led to the correct document. The authors recommend (1) setting stricter guidelines for acceptable citations in course assignments; (2) creating and maintaining scholarly portals for authoritative Web sites with a commitment to long-term access; and (3) continuing to instruct students how to critically evaluate resources

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Abstract

This study compares the range of disciplines of citing journal articles to determine how closely related journals assigned to the same Web of Science research area are. The frequency distribution of disciplines by citing articles provides a signature for a cited journal that permits it to be compared with other journals using similarity comparison techniques. As an initial exploration, citing discipline data for 40 high-impact-factor journals assigned to the "information science and library science" category of the Web of Science were compared across 5 time periods. Similarity relationships were determined using multidimensional scaling and hierarchical cluster analysis to compare the outcomes produced by the proposed citing discipline and established cocitation methods. The maps and clustering outcomes reveal that a number of journals in allied areas of the information science and library science category may not be very closely related to each other or may not be appropriately situated in the category studied. The citing discipline similarity data resulted in similar outcomes with the cocitation data but with some notable differences. Because the citing discipline method relies on a citing perspective different from cocitations, it may provide a complementary way to compare journal similarity that is less labor intensive than cocitation analysis.

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Abstract

In-text frequency-weighted citation counting has been seen as a particularly promising solution to the well-known problem of citation analysis that it treats all citations equally, be they crucial to the citing paper or perfunctory. But what is a good weighting scheme? We compare 12 different in-text citation frequency-weighting schemes in the field of library and information science (LIS) and explore author citation impact patterns based on their performance in these schemes. Our results show that the ranks of authors vary widely with different weighting schemes that favor or are biased against common citation impact patterns-substantiated, applied, or noted. These variations separate LIS authors quite clearly into groups with these impact patterns. With consensus rank limits, the hard upper and lower bounds for reasonable author ranks that they provide suggest that author citation ranks may be subject to something like an uncertainty principle.

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Abstract

In this article, we use innovative full-text citation analysis along with supervised topic modeling and network-analysis algorithms to enhance classical bibliometric analysis and publication/author/venue ranking. By utilizing citation contexts extracted from a large number of full-text publications, each citation or publication is represented by a probability distribution over a set of predefined topics, where each topic is labeled by an author-contributed keyword. We then used publication/citation topic distribution to generate a citation graph with vertex prior and edge transitioning probability distributions. The publication importance score for each given topic is calculated by PageRank with edge and vertex prior distributions. To evaluate this work, we sampled 104 topics (labeled with keywords) in review papers. The cited publications of each review paper are assumed to be "important publications" for the target topic (keyword), and we use these cited publications to validate our topic-ranking result and to compare different publication-ranking lists. Evaluation results show that full-text citation and publication content prior topic distribution, along with the classical PageRank algorithm can significantly enhance bibliometric analysis and scientific publication ranking performance, comparing with term frequency-inverted document frequency (tf-idf), language model, BM25, PageRank, and PageRank + language model (p < .001), for academic information retrieval (IR) systems.

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Abstract

In this paper we investigate the scattering of journals and literature obsolescence reflected in more than 137,000 document delivery requests submitted to a national document delivery service. We first summarize the major findings of the study with regards to the performance of the service. We then identify the "core" journals from which article requests were satisfied and address the following research questions: (a) Does the distribution of (core) journals conform to the Bradford's Law of Scattering? (b) Is there a relationship between usage of journals and impact factors, journals with high impact factors being used more often than the rest? (c) Is there a relationship between usage of journals and total citation counts, journals with high total citation counts being used more often than the rest? (d) What is the median age of use (half-life) of requested articles in general? (e) Do requested articles that appear in core journals get obsolete more slowly? (f) Is there a relationship between obsolescence and journal impact factors, journals with high impact factors being obsolete more slowly? (g) Is there a relationship between obsolescence and total citation counts, journals with high total citation counts being obsolete more slowly? Based an the analysis of findings, we found that the distribution of highly and moderately used journal titles conform to Bradford's Law. The median age of use was 8 years for all requested articles. Ninety percent of the articles requested were 21 years of age or younger. Articles that appeared in 168 core journal titles seem to get obsolete slightly more slowly than those of all titles. We observed no statistically significant correlations between the frequency of journal use and ISI journal impact factors, and between the frequency of journal use and ISI- (Institute for Scientific Information, Philadelphia, PA) cited half-lives for the most heavily used 168 core journal titles. There was a weak correlation between usage of journals and ISI-reported total citation counts. No statistically significant relationship was found between median age of use and journal impact factors and between median age of use and total citation counts. There was a weak negative correlation between ISI journal impact factors and cited half-lives of 168 core journals, and a weak correlation between ISI citation halflives and use half-lives of core journals. No correlation was found between cited half-lives of 168 core journals and their corresponding total citation counts as reported by ISI. Findings of the current study are discussed along with those of other studies.

Date

20. 3.2005 10:54:22

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Abstract

This study explores the usefulness of bibliometric analyses to detect trends in the research profile of a therapeutic drug, for which Aspirin was selected. A total of 22,144 documents dealing with Aspirin and published in journals covered by MEDLINE during the years 19652001 are studied. The research profile of Aspirin over the 37-year period is analyzed through Aspirin subheadings and McSH indexing terms. Half of the documents had Aspirin as a major indexing term, being the main aspects studied therapeutic uses (28% of the documents), pharmacodynamics (26%), adverse effects (18%), and administration and dosage (10%). A frequency data table crossing indexing terms x years is examined by correspondence analysis to obtain time trends, which are shown graphically in a map. Four time periods with a different distribution of indexing terms are identified through cluster analysis. The indexing term profile of every period is obtained by comparison of the distribution of indexing terms of each cluster with that of the whole period by means of the Chi-2 test. The research profile of the drug tends to change faster with time. The most relevant finding is the expanding therapeutic Profile of Aspirin over the period. The main advantages and limitations of the methodology are pointed out.

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Abstract

There is a decrease in the incidence of explicit references to a paper over time, hence the assumption that information ages. In a study which attempts to discover whether information really ages it is necessary to include eponyms, anonyms and footnote references. Reports a pilot study which demonstrates that there is an increase over time in the frequency of use of eponyms

Footnote

Report presented at the 16th National Indian Association of Special Libraries and Information Centres Seminar Special Interest Group Meeting on Informatrics in Bombay, 19-22 Dec 94

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Abstract

Previous studies have repeatedly demonstrated that the relevance of a citing document is related to the number of times with which the source document is cited. Despite the ease with which electronic documents would permit the incorporation of this information into citation-based document search and retrieval systems, the possibilities of repeated citations remain untapped. Part of this under-utilization may be due to the fact that very little is known regarding the pattern of repeated citations in scholarly literature or how this pattern may vary as a function of journal, academic discipline or self-citation. The current research addresses these unanswered questions in order to facilitate the future incorporation of repeated citation information into document search and retrieval systems. Using data mining of electronic texts, the citation characteristics of nine different journals, covering the three different academic fields (economics, computing, and medicine & biology), were characterized. It was found that the frequency (f) with which a reference is cited N or more times within a document is consistent across the sampled journals and academic fields. Self-citation causes an increase in frequency, and this effect becomes more pronounced for large N. The objectivity, automatability, and insensitivity of repeated citations to journal and discipline, present powerful opportunities for improving citation-based document search.

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Abstract

Author self-citations were examined as to their function, frequency, and location in the full text of research articles and compared with external citations. Function analysis was based on manual coding of a small dataset in the field of library and information studies, whereas the analyses by frequency and location used both this small dataset and a large dataset from PubMed Central. Strong evidence was found that self-citations appear more likely to serve as substantial citations in a text than do external citations. This finding challenges previous studies that assumed that self-citations should be discounted or even removed and suggests that self-citations should be given more weight in citation analysis, if anything.

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Abstract

Traces the origin and development of informetrics in the field of library and information science. 'Informatrics' is seen as a generic term to denote studies in which quantitative methods are applied. Discusses various applications of informetrics including citation analysis; impact factor; absolescence and ageing studies; bibliographic coupling; co-citation; and measurement of information such as retrieval performance assessment. Outlines recent developments in informetrics and calls for attention to be paid to the quality of future research in the field to ensure its reliability

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Abstract

We report experiments that use lexical statistics, such as word frequency counts, to discover hidden connections in the medical literature. Hidden connections are those that are unlikely to be found by examination of bibliographic citations or the use of standard indexing methods and yet establish a relationship between topics that might profitably by explored by scientific research. Our experiments were conducted with the MEDLINE medical literature database and follow and extend the work of Swanson

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