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  1. Biebricher, N.; Fuhr, N.; Lustig, G.; Schwantner, M.; Knorz, G.: ¬The automatic indexing system AIR/PHYS : from research to application (1988) 0.16
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    Date
    16. 8.1998 12:51:22
    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.513-517.
  2. Schreiber, M.: Revisiting the g-index : the average number of citations in the g-core (2009) 0.14
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    Abstract
    The g-index is discussed in terms of the average number of citations of the publications in the g-core, showing that it combines features of the h-index and the A-index in one number. For a visualization, data of 8 famous physicists are presented and analyzed. In comparison with the h-index, the g-index increases between 67% and 144%, on average by a factor of 2.
    Object
    g-index
  3. Schreiber, M.: Do we need the g-index? (2013) 0.13
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    Abstract
    Using a very small sample of 8 data sets it was recently shown by De Visscher (2011) that the g-index is very close to the square root of the total number of citations. It was argued that there is no bibliometrically meaningful difference. Using another somewhat larger empirical sample of 26 data sets I show that the difference may be larger and I argue in favor of the g-index.
    Object
    g-index
  4. Schreiber, M.: Fractionalized counting of publications for the g-Index (2009) 0.12
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    Abstract
    L. Egghe ([2008]) studied the h-index (Hirsch index) and the g-index, counting the authorship of cited articles in a fractional way. But his definition of the gF-index for the case that the article count is fractionalized yielded values that were close to or even larger than the original g-index. Here I propose an alternative definition by which the g-index is modified in such a way that the resulting gm-index is always smaller than the original g-index. Based on the interpretation of the g-index as the highest number of articles of a scientist that received on average g or more citations, in the specification of the new gm-index the articles are counted fractionally not only for the rank but also for the average.
    Object
    g-index
  5. Naulleau, E.; Habert, B.; Monteil, M.-G.: Tagging term components with semantic information (1996) 0.11
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    Source
    TKE'96: Terminology and knowledge engineering. Proceedings 4th International Congress on Terminology and Knowledge Engineering, 26.-28.8.1996, Wien. Ed.: C. Galinski u. K.-D. Schmitz
  6. Schreiber, M.: Uncertainties and ambiguities in percentiles and how to avoid them (2013) 0.10
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    Date
    22. 3.2013 19:52:05
  7. Schreiber, M.: ¬An empirical investigation of the g-index for 26 physicists in comparison with the h-index, the A-index, and the R-index (2008) 0.09
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    Abstract
    J.E. Hirsch (2005) introduced the h-index to quantify an individual's scientific research output by the largest number h of a scientist's papers that received at least h citations. To take into account the highly skewed frequency distribution of citations, L. Egghe (2006a) proposed the g-index as an improvement of the h-index. I have worked out 26 practical cases of physicists from the Institute of Physics at Chemnitz University of Technology, and compare the h and g values in this study. It is demonstrated that the g-index discriminates better between different citation patterns. This also can be achieved by evaluating B.H. Jin's (2006) A-index, which reflects the average number of citations in the h-core, and interpreting it in conjunction with the h-index. h and A can be combined into the R-index to measure the h-core's citation intensity. I also have determined the A and R values for the 26 datasets. For a better comparison, I utilize interpolated indices. The correlations between the various indices as well as with the total number of papers and the highest citation counts are discussed. The largest Pearson correlation coefficient is found between g and R. Although the correlation between g and h is relatively strong, the arrangement of the datasets is significantly different depending on whether they are put into order according to the values of either h or g.
  8. Williamson, N.: Knowledge organization for information retrieval : 6th International Study Conference on Classification Research, 16-18 June, 1997 (1997) 0.09
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    Content
    Kurze Berichte über die Vorträge von: J. Mills; E. Svenonius; E. Coates; N. Williamson; A. Neelameghan; M.A. Gopinath; F. Miksa; M. Zeng; M. van der Walt; L. Will, E. Jacob u. H. Albrechtsen; S. Pollitt; C. Beghtol; J.-E. Mai; V. Francu; I.C. McIlwaine; G. Riesthius; J. Mitchell; D. Vizine-Goetz, D. McGarry; H. Olson u. D. Ward; E. Schuegraf; N. Loukachevitch; W. Mustafa-Elhadi u. C. Jouis; A. Carlyle; A.M. Paci; A. Mayne; P. Pekkarinen; A. Evans; C, Roulin u. A. Gilchrist; E. Chmielewska-Gorczyca; P. Gillman; S.D. Clarke; B. Vickery
  9. Christoffel, M.; Schmitt, B.: Accessing Libraries as Easy as a Game (2002) 0.09
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    Date
    22. 2.2003 17:25:39
    22. 2.2003 18:19:01
    Source
    Visual Interfaces to Digital Libraries. Eds.: Börner, K. u. C. Chen
  10. ¬The Second Text Retrieval Conference : TREC-2 (1995) 0.09
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    Content
    Enthält die Beiträge: HARMAN, D.: Overview of the Second Text Retrieval Conference (TREC-2); SPRACK JONES, K.: Reflections on TREC; BUCKLEY, C., J. ALLAN u. G. SALTON: Automatic routing and retrieval using SMART: TREC-2; CALLAN, J.P., W.B. CROFT u. J. BROGLIO: TREC and TIPSTER experiments with INQUERY; ROBERTSON, S.R., S. WALKER u. M.M. HANCOCK-BEAULIEU: Large test collection experiments on an operational, interactive system: OKAPI at TREC; ZOBEL, J., A. MOFFAT, R. WILKINSON u. R. SACKS-DAVIS: Efficient retrieval of partial documents; METTLER, M. u. F. NORDBY: TREC routing experiments with the TRW/Paracel Fast Data Finder; EVANS, D.A. u. R.G. LEFFERTS: CLARIT-TREC experiments; STRZALKOWSKI, T.: Natural language information retrieval; CAID, W.R., S.T. DUMAIS u. S.I. GALLANT: Learned vector-space models for document retrieval; BELKIN, N.J. P. KANTOR, E.A. FOX u. J.A. SHAW: Combining the evidence of multiple query representations for information retrieval
  11. Assem, M. van; Menken, M.R.; Schreiber, G.; Wielemaker, J.; Wielinga, B.: ¬A method for converting thesauri to RDF/OWL (2004) 0.09
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  12. Dabbadie, M.; Blancherie, J.M.: Alexandria, a multilingual dictionary for knowledge management purposes (2006) 0.08
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    Abstract
    Alexandria is an innovation of international impact. It is the only multilingual dictionary for websites and PCs. A double click on a word opens a small window that gives interactive translations between 22 languages and includes meaning, synonyms and associated expressions. It is an ASP application grounded on a semantic network that is portable on any operating system or platform. Behind the application is the Integral Dictionary is the semantic network created by Memodata. Alexandria can be customized with specific vocabulary, descriptive articles, images, sounds, videos, etc. Its domains of application are considerable: e-tourism, online medias, language learning, international websites. Alexandria has also proved to be a basic tool for knowledge management purposes. The application can be customized according to a user or an organization needs. An application dedicated to mobile devices is currently being developed. Future developments are planned in the field of e-tourism in relation with French "pôles de compétitivité".
    Source
    Knowledge organization for a global learning society: Proceedings of the 9th International ISKO Conference, 4-7 July 2006, Vienna, Austria. Hrsg.: G. Budin, C. Swertz u. K. Mitgutsch
  13. Gnoli, C.; Merli, G.; Pavan, G.; Bernuzzi, E.; Priano, M.: Freely faceted classification for a Web-based bibliographic archive : the BioAcoustic Reference Database (2010) 0.08
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    Source
    Wissensspeicher in digitalen Räumen: Nachhaltigkeit - Verfügbarkeit - semantische Interoperabilität. Proceedings der 11. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, Konstanz, 20. bis 22. Februar 2008. Hrsg.: J. Sieglerschmidt u. H.P.Ohly
  14. Güntzer, U.; Jüttner, G.; Seegmüller, G.; Sarré, F.: Automatic thesaurus construction by machine learning from retrieval sessions (1989) 0.08
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  15. Shaw, R.; Golden, P.; Buckland, M.: Using linked library data in working research notes (2015) 0.08
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    Date
    15. 1.2016 19:22:28
    Source
    Linked data and user interaction: the road ahead. Eds.: Cervone, H.F. u. L.G. Svensson
  16. Smith, G.: Newspapers on CD-ROM (1992) 0.08
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    Source
    Serials. 5(1992) no.3, S.17-22
  17. Vetere, G.; Lenzerini, M.: Models for semantic interoperability in service-oriented architectures (2005) 0.08
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    Content
    Vgl.: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5386707&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5386707.
  18. Wielinga, B.; Wielemaker, J.; Schreiber, G.; Assem, M. van: Methods for porting resources to the Semantic Web (2004) 0.07
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  19. Thellefsen, M.: ¬The dynamics of information representation and knowledge mediation (2006) 0.07
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    Source
    Knowledge organization for a global learning society: Proceedings of the 9th International ISKO Conference, 4-7 July 2006, Vienna, Austria. Hrsg.: G. Budin, C. Swertz u. K. Mitgutsch
  20. Martin, G.: Control of electronic resources in Australia (1996) 0.07
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    Series
    Cataloging and classification quarterly; vol.22, nos.3/4
    Source
    Electronic resources: selection and bibliographic control. Ed.: L.-Y.W. Pattie, u. B.J. Cox

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