Search (29 results, page 1 of 2)

  • × language_ss:"d"
  • × theme_ss:"Retrievalalgorithmen"
  • × type_ss:"a"
  1. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.02 
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    Date
    14. 6.2015 22:12:44
    Pages
    S.222-238
  2. Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986) 0.02 
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    Date
    14. 6.2015 22:12:56
    Pages
    S.144-162
  3. Hüther, H.: Selix im DFG-Projekt Kascade (1998) 0.02 
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    Date
    25. 8.2000 19:55:29
    Pages
    S.397-403
  4. 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) 0.01 
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    Date
    31.12.1996 19:29:41
    Source
    Spektrum der Wissenschaft. 1999, H.8, S.44-49
  5. Maylein, L.; Langenstein, A.: Neues vom Relevanz-Ranking im HEIDI-Katalog der Universitätsbibliothek Heidelberg : Perspektiven für bibliothekarische Dienstleistungen (2013) 0.01 
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    Date
    29. 6.2013 18:06:23
    Source
    B.I.T.online. 16(2013) H.3, S.190-200
  6. Kanaeva, Z.: Ranking: Google und CiteSeer (2005) 0.01 
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    Date
    20. 3.2005 16:23:22
    Source
    Information - Wissenschaft und Praxis. 56(2005) H.2, S.87-92
  7. Mayr, P.: Bradfordizing als Re-Ranking-Ansatz in Literaturinformationssystemen (2011) 0.01 
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    Date
    9. 2.2011 17:47:29
    Source
    Information - Wissenschaft und Praxis. 62(2011) H.1, S.3-10
  8. Fuhr, N.: Modelle im Information Retrieval (2023) 0.01 
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    Abstract
    Information-Retrieval-Modelle -(IR-Modelle) spezifizieren, wie zu einer gegebenen Anfrage die Antwortdokumente aus einer Dokumentenkollektion bestimmt werden. Ausgangsbasis jedes Modells sind dabei zunächst bestimmte Annahmen über die Wissensrepräsentation (s. Teil B Methoden und Systeme der Inhaltserschließung) von Fragen und Dokumenten. Hier bezeichnen wir die Elemente dieser Repräsentationen als Terme, wobei es aus der Sicht des Modells egal ist, wie diese Terme aus dem Dokument (und analog aus der von Benutzenden eingegebenen Anfrage) abgeleitet werden: Bei Texten werden hierzu häufig computerlinguistische Methoden eingesetzt, aber auch komplexere automatische oder manuelle Erschließungsverfahren können zur Anwendung kommen. Repräsentationen besitzen ferner eine bestimmte Struktur. Ein Dokument wird meist als Menge oder Multimenge von Termen aufgefasst, wobei im zweiten Fall das Mehrfachvorkommen berücksichtigt wird. Diese Dokumentrepräsentation wird wiederum auf eine sogenannte Dokumentbeschreibung abgebildet, in der die einzelnen Terme gewichtet sein können. Im Folgenden unterscheiden wir nur zwischen ungewichteter (Gewicht eines Terms ist entweder 0 oder 1) und gewichteter Indexierung (das Gewicht ist eine nichtnegative reelle Zahl). Analog dazu gibt es eine Fragerepräsentation; legt man eine natürlichsprachige Anfrage zugrunde, so kann man die o. g. Verfahren für Dokumenttexte anwenden. Alternativ werden auch grafische oder formale Anfragesprachen verwendet, wobei aus Sicht der Modelle insbesondere deren logische Struktur (etwa beim Booleschen Retrieval) relevant ist. Die Fragerepräsentation wird dann in eine Fragebeschreibung überführt.
    Date
    24.11.2022 17:20:29
    Pages
    S.379-398
  9. Agosti, M.; Pretto, L.: ¬A theoretical study of a generalized version of kleinberg's HITS algorithm (2005) 0.01 
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    Date
    31.12.1996 19:29:41
    Source
    Advances in mathematical/formal methods in information retrieval. 8(2005) no.2 , S.219-243
  10. Koopman, R.: ¬Ein OPAC mit Gewichtungsalgorithmen : Der PICA Micro OPC (1996) 0.00 
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    Pages
    S.95-105
  11. Weinstein, A.: Hochprozentig : Tipps and tricks für ein Top-Ranking (2002) 0.00 
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    Source
    Internet Professionell. 2002, H.9, S.66-71
  12. Dreßler, H.: Fuzzy Information Retrieval (2008) 0.00 
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    Source
    Information - Wissenschaft und Praxis. 59(2008) H.6/7, S.351-352
  13. Fuhr, N.: Modelle im Information Retrieval (2013) 0.00 
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    Pages
    S.322-335
  14. Wilhelmy, A.: Phonetische Ähnlichkeitssuche in Datenbanken (1991) 0.00 
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    Abstract
    In dialoggesteuerten Systemen zur Informationswiedergewinnung (Information Retrieval Systems, IRS) kann man - vergröbernd - das Wechselspiel zwischen Mensch und Computer als iterativen Prozess zur Erhöhung von Genauigkeit (Precision) auf der einen und Vollständigkeit (Recall) der Nachweise auf der anderen Seite verstehen. Vorgestellt wird ein maschinell anwendbares Verfahren, das auf phonologische Untersuchungen des Sprachwissenschaftlers Nikolaj S. Trubetzkoy (1890-1938) zurückgeht. In den Grundzügen kann es erheblich zur Verbesserung der Nachweisvollständigkeit beitragen. Dadurch, daß es die 'Ähnlichkeitsumgebungen' von Suchbegriffen in die Recherche mit einbezieht, zeigt es sich vor allem für Systeme mit koordinativer maschineller Indexierung als vorteilhaft. Bei alphabetischen Begriffen erweist sich die Einführung eines solchen zunächst nur auf den Benutzer hin orientierten Verfahrens auch aus technischer Sicht als günstig, da damit die Anzahl der Zugriffe bei den Suchvorgängen auch für große Datenvolumina niedrig gehalten werden kann
    Pages
    S.329-338
  15. Fuhr, N.: Zur Überwindung der Diskrepanz zwischen Retrievalforschung und -praxis (1990) 0.00 
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    Source
    Nachrichten für Dokumentation. 41(1990), S.3-7
  16. Mandl, T.: Web- und Multimedia-Dokumente : Neuere Entwicklungen bei der Evaluierung von Information Retrieval Systemen (2003) 0.00 
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    Information - Wissenschaft und Praxis. 54(2003) H.4, S.203-210
  17. Behnert, C.; Borst, T.: Neue Formen der Relevanz-Sortierung in bibliothekarischen Informationssystemen : das DFG-Projekt LibRank (2015) 0.00 
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    Bibliothek: Forschung und Praxis. 39(2015) H.3, S.384-393
  18. Elsweiler, D.; Kruschwitz, U.: Interaktives Information Retrieval (2023) 0.00 
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  19. Oberhauser, O.; Labner, J.: Relevance Ranking in Online-Katalogen : Informationsstand und Perspektiven (2003) 0.00 
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    Mitteilungen der Vereinigung Österreichischer Bibliothekarinnen und Bibliothekare. 56(2003) H.3/4, S.49-63
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