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  • × theme_ss:"Retrievalalgorithmen"
  1. Sparck Jones, K.: Search term relevance weighting given little relevance information (1979) 0.13 
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.329-338.
  2. Sparck Jones, K.: ¬A statistical interpretation of term specifity and its application in retrieval (1972) 0.12 
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  3. Jones, K.: Linguistic searching versus relevance ranking : DR-LINK and TARGET (1999) 0.10 
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  4. Robertson, S.E.: ¬The probability ranking principle in IR (1977) 0.09 
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willet. San Francisco: Morgan Kaufmann 1997. S.281-286.
  5. Salton, G.; Buckley, C.: Term-weighting approaches in automatic text retrieval (1988) 0.09 
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.323-328.
  6. Robertson, S.E.: OKAPI at TREC-1 (1994) 0.07 
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    Abstract
    Describes the work carried out on the TREC-2 project following the results of the TREC-1 project. Experiments were conducted on the OKAPI experimental text information retrieval system which investigated a number of alternative probabilistic term weighting functions in place of the 'standard' Robertson Sparck Jones weighting functions used in TREC-1
  7. Sparck Jones, K.: IDF term weighting and IR research lessons (2004) 0.07 
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  8. Jones, G.; Robertson, A.M.; Willett, P.: ¬An introduction to genetic algorithms and to their use in information retrieval (1994) 0.06 
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  9. Robertson, M.; Willett, P.: ¬An upperbound to the performance of ranked output searching : optimal weighting of query terms using a genetic algorithms (1996) 0.06 
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    Abstract
    Describes the development of a genetic algorithm (GA) for the assignment of weights to query terms in a ranked output document retrieval system. The GA involves a fitness function that is based on full relevance information, and the rankings resulting from the use of these weights are compared with the Robertson-Sparck Jones F4 retrospective relevance weight
  10. Beaulieu, M.; Jones, S.: Interactive searching and interface issues in the Okapi best match probabilistic retrieval system (1998) 0.05 
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  11. Sparck Jones, K.: ¬A statistical interpretation of term specificity and its application in retrieval (2004) 0.05 
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  12. ¬An introduction to information retrieval (o.J.) 0.04 
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    Abstract
    In the beginning IR was dominated by Boolean retrieval, described in the next section. This could be called the antediluvian period, or generation zero. The first generation of IR research dates from the early sixties, and was dominated by model building, experimentation, and heuristics. The big names were Gerry Salton and Karen Sparck Jones. The second period, which began in the mid-seventies, saw a big shift towards mathematics, and a rise of the IR model based upon probability theory - probabilistic IR. The big name here was, and continues to be, Stephen Robertson. More recently Keith van Rijsbergen has led a group that has developed underlying logical models of IR, but interesting as this new work is, it has not as yet led to results that offer improvements for the IR system builder. Xapian is firmly placed as a system that implements, or tries to implement, the probabilistic IR model. (We say 'tries' because sometimes implementation efficiency and theoretical complexity demand certain short-cuts.)
  13. Maron, M.E.; Kuhns, I.L.: On relevance, probabilistic indexing and information retrieval (1960) 0.04 
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.39-46.
  14. Robertson, S.E.; Sparck Jones, K.: Simple, proven approaches to text retrieval (1997) 0.04 
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  15. Green, R.: Topical relevance relationships : 2: an exploratory study and preliminary typology (1995) 0.03 
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    Abstract
    The assumption of topic matching between user needs and texts topically relevant to those needs is often erroneous. Reports an emprical investigantion of the question 'what relationship types actually account for topical relevance'? In order to avoid the bias to topic matching search strategies, user needs are back generated from a randomly selected subset of the subject headings employed in a user oriented topical concordance. The corresponding relevant texts are those indicated in the concordance under the subject heading. Compares the topics of the user needs with the topics of the relevant texts to determine the relationships between them. Topical relevance relationships include a large variety of relationships, only some of which are matching relationships. Others are examples of paradigmatic or syntagmatic relationships. There appear to be no constraints on the kinds of relationships that can function as topical relevance relationships. They are distinguishable from other types of relationships only on functional grounds
  16. Mandl, T.: Web- und Multimedia-Dokumente : Neuere Entwicklungen bei der Evaluierung von Information Retrieval Systemen (2003) 0.02 
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    Abstract
    Die Menge an Daten im Internet steigt weiter rapide an. Damit wächst auch der Bedarf an qualitativ hochwertigen Information Retrieval Diensten zur Orientierung und problemorientierten Suche. Die Entscheidung für die Benutzung oder Beschaffung von Information Retrieval Software erfordert aussagekräftige Evaluierungsergebnisse. Dieser Beitrag stellt neuere Entwicklungen bei der Evaluierung von Information Retrieval Systemen vor und zeigt den Trend zu Spezialisierung und Diversifizierung von Evaluierungsstudien, die den Realitätsgrad derErgebnisse erhöhen. DerSchwerpunkt liegt auf dem Retrieval von Fachtexten, Internet-Seiten und Multimedia-Objekten.
    Source
    Information - Wissenschaft und Praxis. 54(2003) H.4, S.203-210
  17. Nagelschmidt, M.: Verfahren zur Anfragemodifikation im Information Retrieval (2008) 0.02 
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    Abstract
    Für das Modifizieren von Suchanfragen kennt das Information Retrieval vielfältige Möglichkeiten. Nach einer einleitenden Darstellung der Wechselwirkung zwischen Informationsbedarf und Suchanfrage wird eine konzeptuelle und typologische Annäherung an Verfahren zur Anfragemodifikation gegeben. Im Anschluss an eine kurze Charakterisierung des Fakten- und des Information Retrieval, sowie des Vektorraum- und des probabilistischen Modells, werden intellektuelle, automatische und interaktive Modifikationsverfahren vorgestellt. Neben klassischen intellektuellen Verfahren, wie der Blockstrategie und der "Citation Pearl Growing"-Strategie, umfasst die Darstellung der automatischen und interaktiven Verfahren Modifikationsmöglichkeiten auf den Ebenen der Morphologie, der Syntax und der Semantik von Suchtermen. Darüber hinaus werden das Relevance Feedback, der Nutzen informetrischer Analysen und die Idee eines assoziativen Retrievals auf der Basis von Clustering- und terminologischen Techniken, sowie zitationsanalytischen Verfahren verfolgt. Ein Eindruck für die praktischen Gestaltungsmöglichkeiten der behandelten Verfahren soll abschließend durch fünf Anwendungsbeispiele vermittelt werden.
  18. Fuhr, N.: Zur Überwindung der Diskrepanz zwischen Retrievalforschung und -praxis (1990) 0.02 
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    Abstract
    In diesem Beitrag werden einige Forschungsergebnisse des Information Retrieval vorgestellt, die unmittelbar zur Verbesserung der Retrievalqualität für bereits existierende Datenbanken eingesetzt werden können: Linguistische Algorithmen zur Grund- und Stammformreduktion unterstützen die Suche nach Flexions- und Derivationsformen von Suchtermen. Rankingalgorithmen, die Frage- und Dokumentterme gewichten, führen zu signifikant besseren Retrievalergebnissen als beim Booleschen Retrieval. Durch Relevance Feedback können die Retrievalqualität weiter gesteigert und außerdem der Benutzer bei der sukzessiven Modifikation seiner Frageformulierung unterstützt werden. Es wird eine benutzerfreundliche Bedienungsoberfläche für ein System vorgestellt, das auf diesen Konzepten basiert.
  19. Tober, M.; Hennig, L.; Furch, D.: SEO Ranking-Faktoren und Rang-Korrelationen 2014 : Google Deutschland (2014) 0.02 
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    Abstract
    Dieses Whitepaper beschäftigt sich mit der Definition und Bewertung von Faktoren, die eine hohe Rangkorrelation-Koeffizienz mit organischen Suchergebnissen aufweisen und dient dem Zweck der tieferen Analyse von Suchmaschinen-Algorithmen. Die Datenerhebung samt Auswertung bezieht sich auf Ranking-Faktoren für Google-Deutschland im Jahr 2014. Zusätzlich wurden die Korrelationen und Faktoren unter anderem anhand von Durchschnitts- und Medianwerten sowie Entwicklungstendenzen zu den Vorjahren hinsichtlich ihrer Relevanz für vordere Suchergebnis-Positionen interpretiert.
  20. Behnert, C.; Borst, T.: Neue Formen der Relevanz-Sortierung in bibliothekarischen Informationssystemen : das DFG-Projekt LibRank (2015) 0.02 
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    Abstract
    Das von der DFG geförderte Projekt LibRank erforscht neue Rankingverfahren für bibliothekarische Informationssysteme, die aufbauend auf Erkenntnissen aus dem Bereich Websuche qualitätsinduzierende Faktoren wie z. B. Aktualität, Popularität und Verfügbarkeit von einzelnen Medien berücksichtigen. Die konzipierten Verfahren werden im Kontext eines in den Wirtschaftswissenschaften häufig genutzten Rechercheportals (EconBiz) entwickelt und in einem Testsystem systematisch evaluiert. Es werden Rankingfaktoren, die für den Bibliotheksbereich von besonderem Interesse sind, vorgestellt und exemplarisch Probleme und Herausforderungen aufgezeigt.
    Source
    Bibliothek: Forschung und Praxis. 39(2015) H.3, S.384-393

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