Search (58 results, page 1 of 3)

  • × year_i:[2000 TO 2010}
  • × theme_ss:"Retrievalalgorithmen"
  1. Kanaeva, Z.: Ranking: Google und CiteSeer (2005) 0.04
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    Date
    20. 3.2005 16:23:22
    Language
    d
    Source
    Information - Wissenschaft und Praxis. 56(2005) H.2, S.87-92
  2. Dreßler, H.: Fuzzy Information Retrieval (2008) 0.02
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    Abstract
    Nach einer Erläuterung der Grundlagen der Fuzzylogik wird das Prinzip der unscharfen Suche dargestellt und die Unterschiede zum herkömmlichen Information Retrieval beschrieben. Am Beispiel der Suche nach Steinen für ein Mauerwerk wird gezeigt, wie eine unscharfe Suche in der D&WFuzzydatenbank erfolgreich durchgeführt werden kann und zu eindeutigen Ergebnissen führt.
    Language
    d
    Source
    Information - Wissenschaft und Praxis. 59(2008) H.6/7, S.351-352
  3. Fan, W.; Fox, E.A.; Pathak, P.; Wu, H.: ¬The effects of fitness functions an genetic programming-based ranking discovery for Web search (2004) 0.02
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    Date
    31. 5.2004 19:22:06
  4. Weinstein, A.: Hochprozentig : Tipps and tricks für ein Top-Ranking (2002) 0.02
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    Language
    d
    Source
    Internet Professionell. 2002, H.9, S.66-71
  5. Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000) 0.01
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    Date
    25. 8.2005 17:42:22
  6. Song, D.; Bruza, P.D.: Towards context sensitive information inference (2003) 0.01
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    Date
    22. 3.2003 19:35:46
  7. Shiri, A.A.; Revie, C.: Query expansion behavior within a thesaurus-enhanced search environment : a user-centered evaluation (2006) 0.01
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    Abstract
    The study reported here investigated the query expansion behavior of end-users interacting with a thesaurus-enhanced search system on the Web. Two groups, namely academic staff and postgraduate students, were recruited into this study. Data were collected from 90 searches performed by 30 users using the OVID interface to the CAB abstracts database. Data-gathering techniques included questionnaires, screen capturing software, and interviews. The results presented here relate to issues of search-topic and search-term characteristics, number and types of expanded queries, usefulness of thesaurus terms, and behavioral differences between academic staff and postgraduate students in their interaction. The key conclusions drawn were that (a) academic staff chose more narrow and synonymous terms than did postgraduate students, who generally selected broader and related terms; (b) topic complexity affected users' interaction with the thesaurus in that complex topics required more query expansion and search term selection; (c) users' prior topic-search experience appeared to have a significant effect on their selection and evaluation of thesaurus terms; (d) in 50% of the searches where additional terms were suggested from the thesaurus, users stated that they had not been aware of the terms at the beginning of the search; this observation was particularly noticeable in the case of postgraduate students.
    Date
    22. 7.2006 16:32:43
  8. Mandl, T.: Web- und Multimedia-Dokumente : Neuere Entwicklungen bei der Evaluierung von Information Retrieval Systemen (2003) 0.01
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    Language
    d
    Source
    Information - Wissenschaft und Praxis. 54(2003) H.4, S.203-210
  9. Radev, D.; Fan, W.; Qu, H.; Wu, H.; Grewal, A.: Probabilistic question answering on the Web (2005) 0.01
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  10. Oberhauser, O.; Labner, J.: Relevance Ranking in Online-Katalogen : Informationsstand und Perspektiven (2003) 0.01
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    Language
    d
    Source
    Mitteilungen der Vereinigung Österreichischer Bibliothekarinnen und Bibliothekare. 56(2003) H.3/4, S.49-63
  11. Stock, M.; Stock, W.G.: Internet-Suchwerkzeuge im Vergleich (IV) : Relevance Ranking nach "Popularität" von Webseiten: Google (2001) 0.01
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    Language
    d
    Source
    Password. 2001, H.2, S.20-27
  12. MacFarlane, A.; Robertson, S.E.; McCann, J.A.: Parallel computing for passage retrieval (2004) 0.01
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    Date
    20. 1.2007 18:30:22
  13. Losada, D.E.; Barreiro, A.: Emebedding term similarity and inverse document frequency into a logical model of information retrieval (2003) 0.01
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    Date
    22. 3.2003 19:27:23
  14. Lanvent, A.: Know-how - Suchverfahren : Intelligente Suchmaschinen erzielen mit assoziativen und linguistischen Verfahren beste Ergebnisse. (2004) 0.01
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    Language
    d
    Source
    PC professionell. 2004, H.10, S.134
  15. Lanvent, A.: Praxis - Windows-Suche und Indexdienst : Auch Windows kann bei der Suche den Turbo einlegen: mit dem Indexdienst (2004) 0.01
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    d
    Source
    PC professionell. 2004, H.10, S.134
  16. Lanvent, A.: Licht im Daten Chaos (2004) 0.01
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  17. Ackermann, J.: Knuth-Morris-Pratt (2005) 0.01
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    Abstract
    Im Rahmen des Seminars Suchmaschinen und Suchalgorithmen beschäftigt sich diese Arbeit mit dem Auffinden bestimmter Wörter oder Muster in Texten. Der Begriff "Text" wird hier in einem sehr allgemeinen Sinne als strukturierte Folge beliebiger Länge von Zeichen aus einem endlichen Alphabet verstanden. Somit fällt unter diesen Bereich ganz allgemein die Suche nach einem Muster in einer Sequenz von Zeichen. Beispiele hierfür sind neben der Suche von Wörtern in "literarischen" Texten, z.B. das Finden von Pixelfolgen in Bildern oder gar das Finden von Mustern in DNS-Strängen. Das Anwendungsgebiet für eine solche Suche ist weit gefächert. Man denke hier allein an Texteditoren, Literaturdatenbanken, digitale Lexika oder die besagte DNADatenbank. Betrachtet man allein das 1989 publizierte Oxford English Dictionary mit seinen etwa 616500 definierten Stichworten auf gedruckten 21728 Seiten, so gilt es, einen möglichst effizienten Algorithmus für die Suche in Texten zu nutzen. Der in der Arbeit zugrunde liegende Datentyp ist vom Typ String (Zeichenkette), wobei hier offen gelassen wird, wie der Datentyp programmtechnisch realisiert wird. Algorithmen zur Verarbeitung von Zeichenketten (string processing) umfassen ein bestimmtes Spektrum an Anwendungsgebieten [Ot96, S.617 f.], wie z.B. das Komprimieren, das Verschlüssen, das Analysieren (parsen), das Übersetzen von Texten sowie das Suchen in Texten, welches Thema dieses Seminars ist. Im Rahmen dieser Arbeit wird der Knuth-Morris-Pratt Algorithmus vorgestellt, der wie der ebenfalls in diesem Seminar vorgestellte Boyer-Moore Algorithmus einen effizienten Suchalgorithmus darstellt. Dabei soll ein gegebenes Suchwort oder Muster (pattern) in einer gegeben Zeichenkette erkannt werden (pattern matching). Gesucht werden dabei ein oder mehrere Vorkommen eines bestimmten Suchwortes (exact pattern matching). Der Knuth-Morris-Pratt Algorithmus wurde erstmals 1974 als Institutbericht der Stanford University beschrieben und erschien 1977 in der Fachzeitschrift Journal of Computing unter dem Titel "Fast Pattern Matching in Strings" [Kn77]. Der Algorithmus beschreibt eine Suche in Zeichenketten mit linearer Laufzeit. Der Name des Algorithmus setzt sich aus den Entwicklern des Algorithmus Donald E. Knuth, James H. Morris und Vaughan R. Pratt zusammen.
    Language
    d
  18. Jacso, P.: Testing the calculation of a realistic h-index in Google Scholar, Scopus, and Web of Science for F. W. Lancaster (2008) 0.01
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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
  19. Crestani, F.; Dominich, S.; Lalmas, M.; Rijsbergen, C.J.K. van: Mathematical, logical, and formal methods in information retrieval : an introduction to the special issue (2003) 0.01
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    Date
    22. 3.2003 19:27:36
  20. Furner, J.: ¬A unifying model of document relatedness for hybrid search engines (2003) 0.01
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    Date
    11. 9.2004 17:32:22

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