Search (81 results, page 1 of 5)

  • × theme_ss:"Retrievalalgorithmen"
  1. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.07
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    Date
    14. 6.2015 22:12:44
    Source
    Deutscher Dokumentartag 1985, Nürnberg, 1.-4.10.1985: Fachinformation: Methodik - Management - Markt; neue Entwicklungen, Berufe, Produkte. Bearb.: H. Strohl-Goebel
  2. Chang, C.-H.; Hsu, C.-C.: Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval (1998) 0.04
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    Date
    1. 8.1996 22:08:06
  3. Kanaeva, Z.: Ranking: Google und CiteSeer (2005) 0.04
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    Date
    20. 3.2005 16:23:22
    Source
    Information - Wissenschaft und Praxis. 56(2005) H.2, S.87-92
  4. Fan, W.; Fox, E.A.; Pathak, P.; Wu, H.: ¬The effects of fitness functions an genetic programming-based ranking discovery for Web search (2004) 0.04
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    Date
    31. 5.2004 19:22:06
  5. Wu, H.; Salton, G.: ¬The estimation of term relevance weights using relevance feedback (1981) 0.03
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  6. Hüther, H.: Selix im DFG-Projekt Kascade (1998) 0.03
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    Source
    Knowledge Management und Kommunikationssysteme: Proceedings des 6. Internationalen Symposiums für Informationswissenschaft (ISI '98) Prag, 3.-7. November 1998 / Hochschulverband für Informationswissenschaft (HI) e.V. Konstanz ; Fachrichtung Informationswissenschaft der Universität des Saarlandes, Saarbrücken. Hrsg.: Harald H. Zimmermann u. Volker Schramm
  7. Dreßler, H.: Fuzzy Information Retrieval (2008) 0.03
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    Source
    Information - Wissenschaft und Praxis. 59(2008) H.6/7, S.351-352
  8. Jacso, P.: Testing the calculation of a realistic h-index in Google Scholar, Scopus, and Web of Science for F. W. Lancaster (2008) 0.03
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    Abstract
    This paper focuses on the practical limitations in the content and software of the databases that are used to calculate the h-index for assessing the publishing productivity and impact of researchers. To celebrate F. W. Lancaster's biological age of seventy-five, and "scientific age" of forty-five, this paper discusses the related features of Google Scholar, Scopus, and Web of Science (WoS), and demonstrates in the latter how a much more realistic and fair h-index can be computed for F. W. Lancaster than the one produced automatically. Browsing and searching the cited reference index of the 1945-2007 edition of WoS, which in my estimate has over a hundred million "orphan references" that have no counterpart master records to be attached to, and "stray references" that cite papers which do have master records but cannot be identified by the matching algorithm because of errors of omission and commission in the references of the citing works, can bring up hundreds of additional cited references given to works of an accomplished author but are ignored in the automatic process of calculating the h-index. The partially manual process doubled the h-index value for F. W. Lancaster from 13 to 26, which is a much more realistic value for an information scientist and professor of his stature.
    Object
    h-index
  9. White, H. D.: Co-cited author retrieval and relevance theory : examples from the humanities (2015) 0.02
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  10. Loughran, H.: ¬A review of nearest neighbour information retrieval (1994) 0.02
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  11. Weinstein, A.: Hochprozentig : Tipps and tricks für ein Top-Ranking (2002) 0.02
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    Source
    Internet Professionell. 2002, H.9, S.66-71
  12. Bar-Ilan, J.; Levene, M.: ¬The hw-rank : an h-index variant for ranking web pages (2015) 0.02
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  13. Na, S.-H.; Kang, I.-S.; Roh, J.-E.; Lee, J.-H.: ¬An empirical study of query expansion and cluster-based retrieval in language modeling approach (2007) 0.02
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  14. Radev, D.; Fan, W.; Qu, H.; Wu, H.; Grewal, A.: Probabilistic question answering on the Web (2005) 0.02
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  15. Ding, Y.; Yan, E.; Frazho, A.; Caverlee, J.: PageRank for ranking authors in co-citation networks (2009) 0.02
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    Abstract
    This paper studies how varied damping factors in the PageRank algorithm influence the ranking of authors and proposes weighted PageRank algorithms. We selected the 108 most highly cited authors in the information retrieval (IR) area from the 1970s to 2008 to form the author co-citation network. We calculated the ranks of these 108 authors based on PageRank with the damping factor ranging from 0.05 to 0.95. In order to test the relationship between different measures, we compared PageRank and weighted PageRank results with the citation ranking, h-index, and centrality measures. We found that in our author co-citation network, citation rank is highly correlated with PageRank with different damping factors and also with different weighted PageRank algorithms; citation rank and PageRank are not significantly correlated with centrality measures; and h-index rank does not significantly correlate with centrality measures but does significantly correlate with other measures. The key factors that have impact on the PageRank of authors in the author co-citation network are being co-cited with important authors.
  16. Li, H.; Wu, H.; Li, D.; Lin, S.; Su, Z.; Luo, X.: PSI: A probabilistic semantic interpretable framework for fine-grained image ranking (2018) 0.02
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  17. Mandl, T.: Web- und Multimedia-Dokumente : Neuere Entwicklungen bei der Evaluierung von Information Retrieval Systemen (2003) 0.02
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    Source
    Information - Wissenschaft und Praxis. 54(2003) H.4, S.203-210
  18. Chakrabarti, S.; Dom, B.; Kumar, S.R.; Raghavan, P.; Rajagopalan, S.; Tomkins, A.; Kleinberg, J.M.; Gibson, D.: Neue Pfade durch den Internet-Dschungel : Die zweite Generation von Web-Suchmaschinen (1999) 0.02
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    Source
    Spektrum der Wissenschaft. 1999, H.8, S.44-49
  19. Maylein, L.; Langenstein, A.: Neues vom Relevanz-Ranking im HEIDI-Katalog der Universitätsbibliothek Heidelberg : Perspektiven für bibliothekarische Dienstleistungen (2013) 0.02
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    Source
    B.I.T.online. 16(2013) H.3, S.190-200
  20. Behnert, C.; Borst, T.: Neue Formen der Relevanz-Sortierung in bibliothekarischen Informationssystemen : das DFG-Projekt LibRank (2015) 0.02
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    Source
    Bibliothek: Forschung und Praxis. 39(2015) H.3, S.384-393

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