Search (27 results, page 1 of 2)

  • × theme_ss:"Citation indexing"
  1. Fong, A.C.M.: Mining a Web citation database for document clustering (2002) 0.09 
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    Theme
    Data Mining
  2. He, Y.; Hui, S.C.: Mining a web database for author cocitation analysis (2002) 0.06 
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  3. Kostoff, R.N.; Rio, J.A. del; Humenik, J.A.; Garcia, E.O.; Ramirez, A.M.: Citation mining : integrating text mining and bibliometrics for research user profiling (2001) 0.05 
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    Abstract
    Identifying the users and impact of research is important for research performers, managers, evaluators, and sponsors. It is important to know whether the audience reached is the audience desired. It is useful to understand the technical characteristics of the other research/development/applications impacted by the originating research, and to understand other characteristics (names, organizations, countries) of the users impacted by the research. Because of the many indirect pathways through which fundamental research can impact applications, identifying the user audience and the research impacts can be very complex and time consuming. The purpose of this article is to describe a novel approach for identifying the pathways through which research can impact other research, technology development, and applications, and to identify the technical and infrastructure characteristics of the user population. A novel literature-based approach was developed to identify the user community and its characteristics. The research performed is characterized by one or more articles accessed by the Science Citation Index (SCI) database, beccause the SCI's citation-based structure enables the capability to perform citation studies easily. The user community is characterized by the articles in the SCI that cite the original research articles, and that cite the succeeding generations of these articles as well. Text mining is performed on the citing articles to identify the technical areas impacted by the research, the relationships among these technical areas, and relationships among the technical areas and the infrastructure (authors, journals, organizations). A key component of text mining, concept clustering, was used to provide both a taxonomy of the citing articles' technical themes and further technical insights based on theme relationships arising from the grouping process. Bibliometrics is performed on the citing articles to profile the user characteristics. Citation Mining, this integration of citation bibliometrics and text mining, is applied to the 307 first generation citing articles of a fundamental physics article on the dynamics of vibrating sand-piles. Most of the 307 citing articles were basic research whose main themes were aligned with those of the cited article. However, about 20% of the citing articles were research or development in other disciplines, or development within the same discipline. The text mining alone identified the intradiscipline applications and extradiscipline impacts and applications; this was confirmed by detailed reading of the 307 abstracts. The combination of citation bibliometrics and text mining provides a synergy unavailable with each approach taken independently. Furthermore, text mining is a REQUIREMENT for a feasible comprehensive research impact determination. The integrated multigeneration citation analysis required for broad research impact determination of highly cited articles will produce thousands or tens or hundreds of thousands of citing article Abstracts.
  4. Nicolaisen, J.: Citation analysis (2007) 0.03 
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    Date
    13. 7.2008 19:53:22
  5. Døsen, K.: One more reference on self-reference (1992) 0.03 
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    Date
    7. 2.2005 14:10:22
  6. Belter, C.W.: Citation analysis as a literature search method for systematic reviews (2016) 0.03 
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    Abstract
    Systematic reviews are essential for evaluating biomedical treatment options, but the growing size and complexity of the available biomedical literature combined with the rigor of the systematic review method mean that systematic reviews are extremely difficult and labor-intensive to perform. In this article, I propose a method of searching the literature by systematically mining the various types of citation relationships between articles. I then test the method by comparing its precision and recall to that of 14 published systematic reviews. The method successfully retrieved 74% of the studies included in these reviews and 90% of the studies it could reasonably be expected to retrieve. The method also retrieved fewer than half of the total number of publications retrieved by these reviews and can be performed in substantially less time. This suggests that the proposed method offers a promising complement to traditional text-based methods of literature identification and retrieval for systematic reviews.
  7. Schwartz, F.; Fang, Y.C.: Citation data analysis on hydrogeology (2007) 0.03 
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    Abstract
    This article explores the status of research in hydrogeology using data mining techniques. First we try to explain what citation analysis is and review some of the previous work on citation analysis. The main idea in this article is to address some common issues about citation numbers and the use of these data. To validate the use of citation numbers, we compare the citation patterns for Water Resources Research papers in the 1980s with those in the 1990s. The citation growths for highly cited authors from the 1980s are used to examine whether it is possible to predict the citation patterns for highly-cited authors in the 1990s. If the citation data prove to be steady and stable, these numbers then can be used to explore the evolution of science in hydrogeology. The famous quotation, "If you are not the lead dog, the scenery never changes," attributed to Lee Iacocca, points to the importance of an entrepreneurial spirit in all forms of endeavor. In the case of hydrogeological research, impact analysis makes it clear how important it is to be a pioneer. Statistical correlation coefficients are used to retrieve papers among a collection of 2,847 papers before and after 1991 sharing the same topics with 273 papers in 1991 in Water Resources Research. The numbers of papers before and after 1991 are then plotted against various levels of citations for papers in 1991 to compare the distributions of paper population before and after that year. The similarity metrics based on word counts can ensure that the "before" papers are like ancestors and "after" papers are descendants in the same type of research. This exercise gives us an idea of how many papers are populated before and after 1991 (1991 is chosen based on balanced numbers of papers before and after that year). In addition, the impact of papers is measured in terms of citation presented as "percentile," a relative measure based on rankings in one year, in order to minimize the effect of time.
    Theme
    Data Mining
  8. Van der Veer Martens, B.: Do citation systems represent theories of truth? (2001) 0.02 
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    Date
    22. 7.2006 15:22:28
  9. Safder, I.; Ali, M.; Aljohani, N.R.; Nawaz, R.; Hassan, S.-U.: Neural machine translation for in-text citation classification (2023) 0.02 
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    Abstract
    The quality of scientific publications can be measured by quantitative indices such as the h-index, Source Normalized Impact per Paper, or g-index. However, these measures lack to explain the function or reasons for citations and the context of citations from citing publication to cited publication. We argue that citation context may be considered while calculating the impact of research work. However, mining citation context from unstructured full-text publications is a challenging task. In this paper, we compiled a data set comprising 9,518 citations context. We developed a deep learning-based architecture for citation context classification. Unlike feature-based state-of-the-art models, our proposed focal-loss and class-weight-aware BiLSTM model with pretrained GloVe embedding vectors use citation context as input to outperform them in multiclass citation context classification tasks. Our model improves on the baseline state-of-the-art by achieving an F1 score of 0.80 with an accuracy of 0.81 for citation context classification. Moreover, we delve into the effects of using different word embeddings on the performance of the classification model and draw a comparison between fastText, GloVe, and spaCy pretrained word embeddings.
  10. Chen, C.: Mapping scientific frontiers : the quest for knowledge visualization (2003) 0.02 
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    Footnote
    The title of Chapter 5, On the Shoulders of Giants, implies that knowledge of the structure of scientific frontiers in the immediate past holds the key to a fruitful exploration of people's intellectual assets. Chapter 6, Tracing Competing Paradigms explains how information visualization can draw upon the philosophical framework of paradigm shifts and thereby enable scientists to track the development of Competing paradigms. The final chapter, Tracking Latent Domain Knowledge, turns citation analysis upside down by looking at techniques that may reveal latent domain knowledge. Mapping Scientific Frontiers: The Quest for Knowledge Visualization is an excellent book and is highly recommended. The book convincingly outlines general theories conceming cartography, visual communication, and science mapping-especially how metaphors can make a "big picture"simple and useful. The author likewise Shows how the GSA framework is based not only an technical possibilities but indeed also an the visualization principles presented in the beginning chapters. Also, the author does a fine job of explaining why the mapping of scientific frontiers needs a combined effort from a diverse range of underlying disciplines, such as philosophy of science, sociology of science, scientometrics, domain analyses, information visualization, knowledge discovery, and data mining.
  11. Garfield, E.; Stock, W.G.: Citation Consciousness : Interview with Eugene Garfiels, chairman emeritus of ISI; Philadelphia (2002) 0.02 
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    Source
    Password. 2002, H.6, S.22-25
  12. Larivière, V.; Gingras, Y.; Archambault, E.: ¬The decline in the concentration of citations, 1900-2007 (2009) 0.01 
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    Date
    22. 3.2009 19:22:35
  13. Bensman, S.J.: Eugene Garfield, Francis Narin, and PageRank : the theoretical bases of the Google search engine (2013) 0.01 
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    Date
    17.12.2013 11:02:22
  14. Garfield, E.: Recollections of Irving H. Sher 1924-1996 : Polymath/information scientist extraordinaire (2001) 0.01 
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    Date
    16.12.2001 14:01:22
  15. Van der Veer Martens, B.; Goodrum, G.: ¬The diffusion of theories : a functional approach (2006) 0.01 
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    Date
    22. 7.2006 15:20:01
  16. Tay, A.: ¬The next generation discovery citation indexes : a review of the landscape in 2020 (2020) 0.01 
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    Date
    17.11.2020 12:22:59
  17. Campanario, J.M.: Have referees rejected some of the most-cited articles of all times? (1996) 0.01 
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    Abstract
    In this article a quantitative study is reported on the resistance that scientists may encounter when they do innovative work or when they attempt to publish articles that later become highly cited. A set of 205 commentaries by authors of some of the most-cited papers of all times have been examined in order to identify those articles whose authors encountered difficulty in getting his or her work published. There are 22 commentaries (10,7%) in which authors mention some difficulty or resistance in doing or publishing the research reported in the article. Three of the articles which had problems in being published are the most cited from their respective journals. According the authors' commentaries, although sometimes referees' negative evaluations can help improve the articles, in other instances referees and editors wrongly rejected the highly cited articles
  18. Snyder, H.; Bonzi, S.: Patterns of self-citation across disciplines : 1980-1989 (1998) 0.01 
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    Date
    22. 5.1999 19:33:24
  19. wst: Cut-and-paste-Wissenschaft (2003) 0.01 
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    Content
    "Mikhail Simkin und Vwani Roychowdhury von der University of Califomia, Los Angeles, haben eine in der wissenschaftlichen Gemeinschaft verbreitete Unsitte erstmals quantitativ erfasst. Die Wissenschaftler analysierten die Verbreitung von Druckfehlern in den Literaturlisten wissenschaftlicher Arbeiten (www.arxiv.org/abs/cond-mat/0212043). 78 Prozent aller zitierten Aufsätze - so schätzen die Forscher - haben die zitierenden Wissenschaftler demnach nicht gelesen, sondern nur per 'cut and paste' von einer Vorlage in ihre eigene Literaturliste übernommen. Das könne man beispielsweise abschätzen aus der Analyse fehlerhafter Seitenangaben in der Literaturliste eines 1973 veröffentlichten Aufsatzes über die Struktur zweidimensionaler Kristalle: Dieser Aufsatz ist rund 4300 mal zitiert worden. In 196 Fällen enthalten die Zitate jedoch Fehler in der Jahreszahl, dem Band der Zeitschrift oder der Seitenzahl, die als Indikatoren für cut and paste genommen werden können, denn man kann, obwohl es Milliarden Möglichkeiten gibt, nur 45 verschiedene Arten von Druckfehlern unterscheiden. In erster Näherung ergibt sich eine Obergrenze für die Zahl der `echten Leser' daher aus der Zahl der unterscheidbaren Druckfehler (45) geteilt durch die Gesamtzahl der Publikationen mit Druckfehler (196), das macht etwa 22 Prozent."
  20. Chan, H.C.; Kim, H.-W.; Tan, W.C.: Information systems citation patterns from International Conference on Information Systems articles (2006) 0.01 
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    Date
    3. 1.2007 17:22:03

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