Search (321 results, page 1 of 17)

  • × theme_ss:"Suchmaschinen"
  1. Li, L.; Shang, Y.; Zhang, W.: Improvement of HITS-based algorithms on Web documents 0.16 
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    Content
    Vgl.: http%3A%2F%2Fdelab.csd.auth.gr%2F~dimitris%2Fcourses%2Fir_spring06%2Fpage_rank_computing%2Fp527-li.pdf. Vgl. auch: http://www2002.org/CDROM/refereed/643/.
  2. Gillitzer, B.: Yewno (2017) 0.06 
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    Abstract
    Yewno findet Themen und Konzepte (Suchbegriffe und ihre Abstraktionen) in englischsprachigen digitalen Texten mit Methoden des maschinellen Lernens und der künstlichen Intelligenz. Als Ergebnis Ihrer Suchanfrage werden die Konzepte, die Ihre Anfrage betreffen, in vielfältigen sachlichen Beziehungen als graphisches Netzwerk präsentiert, über das Sie einfach navigieren können. Auch versteckte thematische Beziehungen werden hier sichtbar gemacht, die vom Bekannten zu neuen Entdeckungen führen. Im Rahmen einer Pilotphase können Sie über einen interdisziplinären Ausschnitt aus aktuellen englischsprachigen Fachzeitschriften verschiedenster Fachgebiete recherchieren. Die zu den Themen gehörigen Artikel werden in Ausschnitten unmittelbar angezeigt und können in den meisten Fällen direkt als Volltext aufgerufen werden.
    "Die Bayerische Staatsbibliothek testet den semantischen "Discovery Service" Yewno als zusätzliche thematische Suchmaschine für digitale Volltexte. Der Service ist unter folgendem Link erreichbar: https://www.bsb-muenchen.de/recherche-und-service/suchen-und-finden/yewno/. Das Identifizieren von Themen, um die es in einem Text geht, basiert bei Yewno alleine auf Methoden der künstlichen Intelligenz und des maschinellen Lernens. Dabei werden sie nicht - wie bei klassischen Katalogsystemen - einem Text als Ganzem zugeordnet, sondern der jeweiligen Textstelle. Die Eingabe eines Suchwortes bzw. Themas, bei Yewno "Konzept" genannt, führt umgehend zu einer grafischen Darstellung eines semantischen Netzwerks relevanter Konzepte und ihrer inhaltlichen Zusammenhänge. So ist ein Navigieren über thematische Beziehungen bis hin zu den Fundstellen im Text möglich, die dann in sogenannten Snippets angezeigt werden. In der Test-Anwendung der Bayerischen Staatsbibliothek durchsucht Yewno aktuell 40 Millionen englischsprachige Dokumente aus Publikationen namhafter Wissenschaftsverlage wie Cambridge University Press, Oxford University Press, Wiley, Sage und Springer, sowie Dokumente, die im Open Access verfügbar sind. Nach der dreimonatigen Testphase werden zunächst die Rückmeldungen der Nutzer ausgewertet. Ob und wann dann der Schritt von der klassischen Suchmaschine zum semantischen "Discovery Service" kommt und welche Bedeutung Anwendungen wie Yewno in diesem Zusammenhang einnehmen werden, ist heute noch nicht abzusehen. Die Software Yewno wurde vom gleichnamigen Startup in Zusammenarbeit mit der Stanford University entwickelt, mit der auch die Bayerische Staatsbibliothek eng kooperiert. [Inetbib-Posting vom 22.02.2017].
    Date
    22. 2.2017 10:16:49
  3. Mandl, T.: Qualität als neue Dimension im Information Retrieval : Das AQUAINT Projekt (2005) 0.03 
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    Abstract
    Die Bewertung von Internet-Dokumenten nach ihrer Qualität wird im Zeitalter verteilten und in großen Mengen vorliegenden Wissens zunehmend an Software delegiert. Dies gilt besonders bei der Suche nach Informationen etwa mit Internet-Suchmaschinen, bei der Qualitätsbewertungssysteme als Filter einsetzbar sind. Die starke Heterogenität der Qua-lität von Internet-Dokumenten lässt hier einen besonderen Mehrwert erwarten. Experimentelle Systeme behandeln verschiedeneAspekte und nutzen unterschiedliche Definitionen von Oualität. Im Einsatz befindliche Systeme greifen zur Zeit auf einzelne und begrenzte Aspekte von Qualität zu. Insbesondere die Analyse der Verlinkungsstruktur des Internets bietet einen Ansatzpunkt für existierende Systeme. Die Komplexität des Qualitätskonzepts und der Dokumente erfordert jedoch auf mehreren Aspekten beruhende Definitionen. Im Projekt AQUAINT (Automatische Qualitätsabschätzung für Internet Ressourcen) werden Oualitätsdefinitionen aus menschlichen Urteilen abgeleitet. Formale Eigenschaften von Internet-Seiten werden automatisch extrahiert. Menschliche Oualitätsurteile unterschiedlichster Art bilden die Grundlage für Verfahren des maschinellen Lernens. So entstehen Klassifikationssysteme, welche die menschlichen Urteile so weit wie möglich simulieren. Diese werden in prototypischen Such- und Filtersystemen integriert.
  4. Perez, E.: dtSearch: the little search engine that could (2004) 0.02 
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    Source
    Online. 29(2005) no.1, S.28-
  5. Höfer, W.: Detektive im Web (1999) 0.02 
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    Date
    22. 8.1999 20:22:06
    Source
    Internet Professionell. 1999, H.9, S.28-36
  6. tw: Power-Sucher für Power-Surfer : Fireball (1997) 0.01 
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    Date
    1. 8.1997 14:03:29
    Source
    Com!. 1997, H.8, S.28
  7. Vidmar, D.J.: Darwin on the Web : the evolution of search tools (1999) 0.01 
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    Source
    Computers in libraries. 19(1999) no.5, S.22-28
  8. Sauer, D.: Alles schneller finden (2001) 0.01 
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    Date
    1. 8.1997 14:03:29
    11.11.2001 17:25:22
    Source
    Com!online. 2001, H.12, S.24-29
  9. Loeper, D. von: Sherlock Holmes im Netz (1997) 0.01 
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    Date
    22. 2.1997 19:50:29
  10. Kiefer, A.: ¬Die Googlewhacker (2002) 0.01 
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    Date
    20. 3.2002 10:28:22
    Source
    Frankfurter Rundschau. Nr.66 vom 19.3.2002, S.29
  11. Schneider, N.: ¬Die Notwendigkeit der Suchmaschinenregulierung aus Sicht eines Regulierers (2007) 0.01 
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    Date
    13. 5.2007 10:29:29
    5. 6.2007 11:28:03
  12. Dasgupta, S.K.: Search engines : tooles for exploring the Internet (1998) 0.01 
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    Date
    8. 5.1999 19:28:29
  13. Haring, M.; Rudaev, A.; Lewandowski, D.: Google & Co. : wie die "Search Studies" an der HAW Hamburg unserem Nutzungsverhalten auf den Zahn fühlen: Blickpunkt angewandte Forschung (2022) 0.01 
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  14. Altmann, O.; Pöhl, S.: Information Retrieval im Internet : bekannte Suchdienste und ihre Eigenschaften (1997) 0.01 
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  15. Lewandowski, D.: Alles nur noch Google? : Entwicklungen im Bereich der WWW-Suchmaschinen (2002) 0.01 
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  16. Becker, F.: Internet-Suchmaschinen : Funktionsweise und Beurteilung (1999) 0.01 
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  17. Zins, C.: Models for classifying Internet resources (2002) 0.01 
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  18. Bertelmann, R.; Rusch-Feja, D.: Informationsretrieval im Internet : Surfen, Browsen, Suchen - mit einem Überblick über strukturierte Informationsangebote (1997) 0.01 
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  19. Marchiori, M.: ¬The quest for correct information on the Web : hyper search engines (1997) 0.01 
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    1. 8.1996 22:08:06
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    Computer networks and ISDN systems. 29(1997) no.8, S.1225-1235
  20. Chang, C.-H.; Hsu, C.-C.: Customizable multi-engine search tool with clustering (1997) 0.01 
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    1. 8.1996 22:08:06
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