Search (19 results, page 1 of 1)

  • × year_i:[2000 TO 2010}
  • × 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. 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
  6. 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
  7. 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.
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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."
  13. 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
  14. H-Index auch im Web of Science (2008) 0.01
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    Date
    6. 4.2008 19:04:22
  15. Mingers, J.; Burrell, Q.L.: Modeling citation behavior in Management Science journals (2006) 0.01
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    Date
    26.12.2007 19:22:05
  16. Ma, N.; Guan, J.; Zhao, Y.: Bringing PageRank to the citation analysis (2008) 0.01
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    Date
    31. 7.2008 14:22:05
  17. Hayer, L.: Lazarsfeld zitiert : eine bibliometrische Analyse (2008) 0.01
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    Date
    22. 6.2008 12:54:12
  18. Wildner, B.: Web of Science - Scopus : Auf der Suche nach Zitierungen (2006) 0.01
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    Date
    4. 6.2006 17:22:15
  19. Ahlgren, P.; Jarneving, B.; Rousseau, R.: Requirements for a cocitation similarity measure, with special reference to Pearson's correlation coefficient (2003) 0.01
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    Date
    9. 7.2006 10:22:35