Search (185 results, page 4 of 10)

  • × theme_ss:"Retrievalalgorithmen"
  1. Carpineto, C.; Romano, G.: Information retrieval through hybrid navigation of lattice representations (1996) 0.01 
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    Source
    International journal of human-computer studies. 45(1996) no.5, S.553-578
  2. Lalmas, M.; Ruthven, I.: Representing and retrieving structured documents using the Dempster-Shafer theory of evidence : modelling and evaluation (1998) 0.01 
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    Journal of documentation. 54(1998) no.5, S.529-565
  3. Rajashekar, T.B.; Croft, W.B.: Combining automatic and manual index representations in probabilistic retrieval (1995) 0.01 
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    Journal of the American Society for Information Science. 46(1995) no.4, S.272-283
  4. Hancock-Beaulieu, M.; Walker, S.: ¬An evaluation of automatic query expansion in an online library catalogue (1992) 0.01 
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    Journal of documentation. 48(1992) no.4, S.406-421
  5. Davis, C.H.; McKim, G.W.: Systematic weighting and ranking : cutting the Gordian knot (1999) 0.01 
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    Journal of the American Society for Information Science. 50(1999) no.7, S.626-628
  6. Wollf, J.G.: ¬A scalable technique for best-match retrieval of sequential information using metrics-guided search (1994) 0.01 
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    Journal of information science. 20(1994) no.1, S.16-28
  7. Boughanem, M.; Chrisment, C.; Tamine, L.: On using genetic algorithms for multimodal relevance optimization in information retrieval (2002) 0.01 
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    Journal of the American Society for Information Science and Technology. 53(2002) no.11, S.934-943
  8. Heinz, S.; Zobel, J.: Efficient single-pass index construction for text databases (2003) 0.01 
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    Journal of the American Society for Information Science and technology. 54(2003) no.8, S.713-729
  9. Losee, R.M.; Church Jr., L.: Are two document clusters better than one? : the cluster performance question for information retrieval (2005) 0.01 
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    Journal of the American Society for Information Science and Technology. 56(2005) no.1, S.106-108
  10. Sparck Jones, K.: ¬A statistical interpretation of term specificity and its application in retrieval (2004) 0.01 
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    Journal of documentation. 60(2004) no.5, S.493-502
  11. Robertson, S.: Understanding inverse document frequency : on theoretical arguments for IDF (2004) 0.01 
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    Journal of documentation. 60(2004) no.5, S.503-520
  12. López-Herrera, A.G.; Herrera-Viedma, E.; Herrera, F.: ¬A study of the use of multi-objective evolutionary algorithms to learn Boolean queries : a comparative study (2009) 0.01 
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    Journal of the American Society for Information Science and Technology. 60(2009) no.6, S.1192-1207
  13. Dang, E.K.F.; Luk, R.W.P.; Allan, J.: ¬A retrieval model family based on the probability ranking principle for ad hoc retrieval (2022) 0.01 
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    Journal of the Association for Information Science and Technology. 73(2022) no.8, S.1140-1154
  14. Stock, M.; Stock, W.G.: Internet-Suchwerkzeuge im Vergleich (IV) : Relevance Ranking nach "Popularität" von Webseiten: Google (2001) 0.01 
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    Abstract
    In unserem Retrievaltest von Suchwerkzeugen im World Wide Web (Password 11/2000) schnitt die Suchmaschine Google am besten ab. Im Vergleich zu anderen Search Engines setzt Google kaum auf Informationslinguistik, sondern auf Algorithmen, die sich aus den Besonderheiten der Web-Dokumente ableiten lassen. Kernstück der informationsstatistischen Technik ist das "PageRank"- Verfahren (benannt nach dem Entwickler Larry Page), das aus der Hypertextstruktur des Web die "Popularität" von Seiten anhand ihrer ein- und ausgehenden Links berechnet. Google besticht durch das Angebot intuitiv verstehbarer Suchbildschirme sowie durch einige sehr nützliche "Kleinigkeiten" wie die Angabe des Rangs einer Seite, Highlighting, Suchen in der Seite, Suchen innerhalb eines Suchergebnisses usw., alles verstaut in einer eigenen Befehlsleiste innerhalb des Browsers. Ähnlich wie RealNames bietet Google mit dem Produkt "AdWords" den Aufkauf von Suchtermen an. Nach einer Reihe von nunmehr vier Password-Artikeln über InternetSuchwerkzeugen im Vergleich wollen wir abschließend zu einer Bewertung kommen. Wie ist der Stand der Technik bei Directories und Search Engines aus informationswissenschaftlicher Sicht einzuschätzen? Werden die "typischen" Internetnutzer, die ja in der Regel keine Information Professionals sind, adäquat bedient? Und können auch Informationsfachleute von den Suchwerkzeugen profitieren?
  15. Hüther, H.: Selix im DFG-Projekt Kascade (1998) 0.01 
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    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
  16. Oberhauser, O.; Labner, J.: Relevance Ranking in Online-Katalogen : Informationsstand und Perspektiven (2003) 0.01 
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    Mitteilungen der Vereinigung Österreichischer Bibliothekarinnen und Bibliothekare. 56(2003) H.3/4, S.49-63
  17. Weller, K.; Stock, W.G.: Transitive meronymy : automatic concept-based query expansion using weighted transitive part-whole relations (2008) 0.01 
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    Abstract
    Transitive Meronymie. Automatische begriffsbasierte Suchanfrageerweiterung unter Nutzung gewichteter transitiver Teil-Ganzes-Relationen. Unsere theoretisch orientierte Arbeit isoliert transitive Teil-Ganzes-Beziehungen. Wir diskutieren den Einsatz der Meronymie bei der automatischen begriffsbasierten Suchanfrageerweiterung im Information Retrieval. Aus praktischen Gründen schlagen wir vor, die Bestandsrelationen zu spezifizieren und die einzelnen Arten mit unterschiedlichen Gewichtungswerten zu versehen, die im Retrieval genutzt werden. Für das Design von Wissensordnungen ist bedeutsam, dass innerhalb der Begriffsleiter einer Abstraktionsrelation ein Begriff alle seine Teile (sowie alle transitiven Teile der Teile) an seine Unterbegriffe vererbt.
    Source
    Information - Wissenschaft und Praxis. 59(2008) H.3, S.165-170
  18. Reimer, U.: Empfehlungssysteme (2023) 0.01 
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    Abstract
    Mit der wachsenden Informationsflut steigen die Anforderungen an Informationssysteme, aus der Menge potenziell relevanter Information die in einem bestimmten Kontext relevanteste zu selektieren. Empfehlungssysteme spielen hier eine besondere Rolle, da sie personalisiert - d. h. kontextspezifisch und benutzerindividuell - relevante Information herausfiltern können. Definition: Ein Empfehlungssystem empfiehlt einem Benutzer bzw. einer Benutzerin in einem definierten Kontext aus einer gegebenen Menge von Empfehlungsobjekten eine Teilmenge als relevant. Empfehlungssysteme machen Benutzer auf Objekte aufmerksam, die sie möglicherweise nie gefunden hätten, weil sie nicht danach gesucht hätten oder sie in der schieren Menge an insgesamt relevanter Information untergegangen wären.
    Source
    Grundlagen der Informationswissenschaft. Hrsg.: Rainer Kuhlen, Dirk Lewandowski, Wolfgang Semar und Christa Womser-Hacker. 7., völlig neu gefasste Ausg
  19. Gauch, S.; Smith, J.B.: ¬An expert system for automatic query reformation (1993) 0.01 
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    Journal of the American Society for Information Science. 44(1993) no.3, S.124-136
  20. Rada, R.; Barlow, J.; Potharst, J.; Zanstra, P.; Bijstra, D.: Document ranking using an enriched thesaurus (1991) 0.01 
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    Source
    Journal of documentation. 47(1991) no.3, S.240-253

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