-
Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986)
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- Date
- 14. 6.2015 22:12:44
- Pages
- S.222-238
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Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986)
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- 14. 6.2015 22:12:56
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- S.144-162
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Tober, M.; Hennig, L.; Furch, D.: SEO Ranking-Faktoren und Rang-Korrelationen 2014 : Google Deutschland (2014)
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- 13. 9.2014 14:45:22
- Pages
- 91 S
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Kanaeva, Z.: Ranking: Google und CiteSeer (2005)
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- Date
- 20. 3.2005 16:23:22
- Source
- Information - Wissenschaft und Praxis. 56(2005) H.2, S.87-92
-
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)
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- Spektrum der Wissenschaft. 1999, H.8, S.44-49
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Koopman, R.: ¬Ein OPAC mit Gewichtungsalgorithmen : Der PICA Micro OPC (1996)
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- Pages
- S.95-105
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Hüther, H.: Selix im DFG-Projekt Kascade (1998)
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- Pages
- S.397-403
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Weinstein, A.: Hochprozentig : Tipps and tricks für ein Top-Ranking (2002)
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- Internet Professionell. 2002, H.9, S.66-71
-
Dreßler, H.: Fuzzy Information Retrieval (2008)
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- Information - Wissenschaft und Praxis. 59(2008) H.6/7, S.351-352
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Fuhr, N.: Modelle im Information Retrieval (2013)
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- Pages
- S.322-335
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Wilhelmy, A.: Phonetische Ähnlichkeitssuche in Datenbanken (1991)
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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
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Fuhr, N.: Zur Überwindung der Diskrepanz zwischen Retrievalforschung und -praxis (1990)
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- Source
- Nachrichten für Dokumentation. 41(1990), S.3-7
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Mandl, T.: Web- und Multimedia-Dokumente : Neuere Entwicklungen bei der Evaluierung von Information Retrieval Systemen (2003)
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- Source
- Information - Wissenschaft und Praxis. 54(2003) H.4, S.203-210
-
Maylein, L.; Langenstein, A.: Neues vom Relevanz-Ranking im HEIDI-Katalog der Universitätsbibliothek Heidelberg : Perspektiven für bibliothekarische Dienstleistungen (2013)
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- Source
- B.I.T.online. 16(2013) H.3, S.190-200
-
Behnert, C.; Borst, T.: Neue Formen der Relevanz-Sortierung in bibliothekarischen Informationssystemen : das DFG-Projekt LibRank (2015)
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- Source
- Bibliothek: Forschung und Praxis. 39(2015) H.3, S.384-393
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Elsweiler, D.; Kruschwitz, U.: Interaktives Information Retrieval (2023)
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- Pages
- S.403-412
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Fuhr, N.: Modelle im Information Retrieval (2023)
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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.
- Pages
- S.379-398
-
Oberhauser, O.; Labner, J.: Relevance Ranking in Online-Katalogen : Informationsstand und Perspektiven (2003)
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- Source
- Mitteilungen der Vereinigung Österreichischer Bibliothekarinnen und Bibliothekare. 56(2003) H.3/4, S.49-63
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Hora, M.: Methoden für das Ranking in Discovery-Systemen (2018)
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- Source
- Perspektive Bibliothek. 7(2018) H.2, S.2-23
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Behnert, C.; Plassmeier, K.; Borst, T.; Lewandowski, D.: Evaluierung von Rankingverfahren für bibliothekarische Informationssysteme (2019)
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- Source
- Information - Wissenschaft und Praxis. 70(2019) H.1, S.14-23