Search (276 results, page 1 of 14)

  • × theme_ss:"Suchmaschinen"
  1. MacLeod, R.: Promoting a subject gateway : a case study from EEVL (Edinburgh Engineering Virtual Library) (2000) 0.05 
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    Date
    22. 6.2002 19:40:22
  2. Hüskes, R.; Kleber, D.: ¬Den Server im Griff (1999) 0.04 
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    Date
    22. 8.1999 21:21:10
  3. Gardner, T.; Iannella, R.: Architecture and software solutions (2000) 0.03 
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    Date
    22. 6.2002 19:38:24
  4. Dresel, R.; Hörnig, D.; Kaluza, H.; Peter, A.; Roßmann, A.; Sieber, W.: Evaluation deutscher Web-Suchwerkzeuge : Ein vergleichender Retrievaltest (2001) 0.03 
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    Abstract
    Die deutschen Suchmaschinen, Abacho, Acoon, Fireball und Lycos sowie die Web-Kataloge Web.de und Yahoo! werden einem Qualitätstest nach relativem Recall, Precision und Availability unterzogen. Die Methoden der Retrievaltests werden vorgestellt. Im Durchschnitt werden bei einem Cut-Off-Wert von 25 ein Recall von rund 22%, eine Precision von knapp 19% und eine Verfügbarkeit von 24% erreicht
  5. Tober, M.; Hennig, L.; Furch, D.: SEO Ranking-Faktoren und Rang-Korrelationen 2014 : Google Deutschland (2014) 0.03 
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    Date
    13. 9.2014 14:45:22
    Type
    r
  6. Hancock, B.: Subject-specific search engines : using the Harvest system to gather and maintain information on the Internet (1998) 0.03 
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    Date
    6. 3.1997 16:22:15
  7. Kohkemper, R.: Studenten-Charme im Big Business : 200 Millionen Anfragen pro Tag - die erfolgreichste Suchmaschine des Internet (2004) 0.03 
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    Content
    SUCHMETHODE PageRank Technologie nennen die Google-Gründer ihre Such-Methode. Nach der Eingabe eines Suchbegriffes werden die 3 Milliarden Webseiten, die Google erreichen kann, damit verglichen. Der Kern der Such-Methode ist dabei, dass Google einen Link von der Seite A zu Seite B als ein "Votum" von der Seite A zu Seite B wertet. Wer also auf seiner Webseite mit einem Link eine andere Seite empfiehlt bzw. darauf verweist, verändert den Stellenwert einer Seite in der Google-Liste. Allerdings verlässt sich Google nicht allein auf die Voten anderer Seiten. Mitarbeiter des Anbieters schätzen die Wichtigkeit einer Seite über dieses Votum ein. Außerdem wird von Google die Seite bewertet, von der das Votum stammt. Ein Beispiel für die Manipulierbarkeit von Google: Wer im weltweiten Netz "miserable failure" ("erbärmlicher Versager") eingibt, landet bei der Biografie von George W. Bush. Zahlreiche Webmaster haben also einen Link zum Weißen Haus gesetzt und und mit dem Suchnamen "miserable failure" unterlegt.
    Date
    3. 5.1997 8:44:22
    13. 6.2004 14:34:22
  8. Bertelmann, R.; Rusch-Feja, D.: Informationsretrieval im Internet : Surfen, Browsen, Suchen - mit einem Überblick über strukturierte Informationsangebote (1997) 0.03 
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    Date
    9. 7.2000 11:31:22
  9. Heery, R.: Information gateways : collaboration and content (2000) 0.03 
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    Date
    22. 6.2002 19:38:54
  10. Li, L.; Shang, Y.; Zhang, W.: Improvement of HITS-based algorithms on Web documents 0.03 
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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/.
  11. Schulz, U.: Search engine usability : über die Nutzungsqualität von Suchmaschinen (2001) 0.02 
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    Abstract
    Web Usability konnte sich als Forschungsbereich zunächst nur in größeren Firmen der Internet-Branche wie z. B. Icon Medialab etablieren. Mit zunehmender Konkurrenz und ersten Pleiten im E-Commerce entsteht die Einsicht oder bittere Erfahrung, dass bunte, multimediale Internetpräsenz nicht alles ist: Die Nutzungsqualität von Websites tritt in den Vordergrund und damit ein neuer Arbeitsmarkt für Informationsarchitekten und HCI- bzw. Usability-Spezialisten. Suchmaschinen sind eine besondere Kategorie von Website. Sie entstehen in einem Spannungsfeld zweier unterschiedlicher Interessen: Mit kommerziellen Websites teilen sie den Wettbewerb um Kunden und die Art der Kunden: den ungeduldigen gelegentlichen Anwender. Mit der akademischen Welt teilen sie das forschende Bemühen um die Optimierung von Features und Funktionen und die déformation professionelle der Eingeweihten. Am Beispiel zweier Untersuchungen wird deutlich, wie es der richtige 'Methodenmix' ermöglicht, bei der Evaluation der Nutzungsqualität von Suchmaschinen ganz unterschiedlicher Art (a) diesen Interessenkonflikt bewusst zu machen, (b) Empathie für die Nöte der Kunden zu entwickeln und c) allgemeingültige Kriterien zur Entwicklung gebrauchstauglicher Suchmaschinen aufzustellen. Beide Untersuchungen fanden im Rahmen des Projekts Jorschung und Forschungsumgebung für Web Usability« unter Mitwirkung von Studierenden am Fachbereich Bibliothek und Information der FH Hamburg statt
    Source
    Information Research & Content Management: Orientierung, Ordnung und Organisation im Wissensmarkt; 23. DGI-Online-Tagung der DGI und 53. Jahrestagung der Deutschen Gesellschaft für Informationswissenschaft und Informationspraxis e.V. DGI, Frankfurt am Main, 8.-10.5.2001. Proceedings. Hrsg.: R. Schmidt
  12. Dominich, S.; Skrop, A.: PageRank and interaction information retrieval (2005) 0.02 
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    Abstract
    The PageRank method is used by the Google Web search engine to compute the importance of Web pages. Two different views have been developed for the Interpretation of the PageRank method and values: (a) stochastic (random surfer): the PageRank values can be conceived as the steady-state distribution of a Markov chain, and (b) algebraic: the PageRank values form the eigenvector corresponding to eigenvalue 1 of the Web link matrix. The Interaction Information Retrieval (1**2 R) method is a nonclassical information retrieval paradigm, which represents a connectionist approach based an dynamic systems. In the present paper, a different Interpretation of PageRank is proposed, namely, a dynamic systems viewpoint, by showing that the PageRank method can be formally interpreted as a particular case of the Interaction Information Retrieval method; and thus, the PageRank values may be interpreted as neutral equilibrium points of the Web.
  13. Sarigil, E.; Sengor Altingovde, I.; Blanco, R.; Barla Cambazoglu, B.; Ozcan, R.; Ulusoy, Ö.: Characterizing, predicting, and handling web search queries that match very few or no results (2018) 0.02 
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  14. Carrière, S.J.; Kazman, R.: Webquery : searching and visualising the Web through connectivity (1997) 0.02 
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    Date
    1. 8.1996 22:08:06
  15. Golderman, G.M.; Connolly, B.: Between the book covers : going beyond OPAC keyword searching with the deep linking capabilities of Google Scholar and Google Book Search (2004/05) 0.02 
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    2.12.2007 19:39:22
  16. Rowlands, I.; Nicholas, D.; Williams, P.; Huntington, P.; Fieldhouse, M.; Gunter, B.; Withey, R.; Jamali, H.R.; Dobrowolski, T.; Tenopir, C.: ¬The Google generation : the information behaviour of the researcher of the future (2008) 0.02 
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  17. Baeza-Yates, R.; Boldi, P.; Castillo, C.: Generalizing PageRank : damping functions for linkbased ranking algorithms (2006) 0.02 
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    16. 1.2016 10:22:28
  18. Mayr, P.: Google Scholar als akademische Suchmaschine (2009) 0.02 
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    Abstract
    Neben den klassischen Informationsanbietern Bibliothek, Fachinformation und den Verlagen sind Internetsuchmaschinen inzwischen fester Bestandteil bei der Recherche nach wissenschaftlicher Information. Scirus (Elsevier, 2004) und Google Scholar sind zwei Beispiele für Suchdienste kommerzieller Suchmaschinen-Unternehmen, die eine Einschränkung auf den wissenschaftlichen Dokumentenraum anstreben und nennenswerte Dokumentzahlen in allen Disziplinen generieren. Der Vergleich der Treffermengen für beliebige Suchthemen zeigt, dass die Wahl des Suchsystems, des Dokumentenpools und der Dokumenttypen entscheidenden Einfluss auf die Relevanz und damit letztlich auch die Akzeptanz des Suchergebnisses hat. Tabelle 1 verdeutlicht die Mengenunterschiede am Beispiel der Trefferergebnisse für die Suchbegriffe "search engines" bzw. "Suchmaschinen" in der allgemeinen Internetsuchmaschine Google, der wissenschaftlichen Suchmaschine Google Scholar (GS) und der größten fachübergreifenden bibliographischen Literaturdatenbank Web of Science (WoS). Der Anteil der Dokumente, die in diesem Fall eindeutig der Wissenschaft zuzuordnen sind (siehe GS und insbesondere WoS in Tabelle 1), liegt gegenüber der allgemeinen Websuche lediglich im Promille-Bereich. Dieses Beispiel veranschaulicht, dass es ausgesprochen problematisch sein kann, fachwissenschaftliche Fragestellungen ausschließlich mit Internetsuchmaschinen zu recherchieren. Der Anteil der fachwissenschaftlich relevanten Dokumente in diesem Trefferpool ist i. d. R. sehr gering. Damit sinkt die Wahrscheinlichkeit, wissenschaftlich relevantes (z. B. einen Zeitschriftenaufsatz) auf den ersten Trefferseiten zu finden, deutlich ab.
    Die drei oben genannten Suchsysteme (Google, GS und WoS) unterscheiden sich in mehrerlei Hinsicht fundamental und eignen sich daher gut, um in die Grundthematik dieses Artikels einzuleiten. Die obigen Suchsysteme erschließen zunächst unterschiedliche Suchräume, und dies auf sehr spezifische Weise. Während Google frei zugängliche und über Hyperlink adressierbare Dokumente im Internet erfasst, gehen die beiden akademischen Suchsysteme deutlich selektiver bei der Inhaltserschließung vor. Google Scholar erfasst neben frei zugänglichen elektronischen Publikationstypen im Internet hauptsächlich wissenschaftliche Dokumente, die direkt von den akademischen Verlagen bezogen werden. Das WoS, das auf den unterschiedlichen bibliographischen Datenbanken und Zitationsindizes des ehemaligen "Institute for Scientific Information" (ISI) basiert, selektiert gegenüber den rein automatischen brute-force-Ansätzen der Internetsuchmaschine über einen qualitativen Ansatz. In den Datenbanken des WoS werden ausschließlich internationale Fachzeitschriften erfasst, die ein kontrolliertes Peer-Review durchlaufen. Insgesamt werden ca. 12.000 Zeitschriften ausgewertet und über die Datenbank verfügbar gemacht. Wie bereits erwähnt, spielt neben der Abgrenzung der Suchräume und Dokumenttypen die Zugänglichkeit und Relevanz der Dokumente eine entscheidende Bedeutung für den Benutzer. Die neueren technologischen Entwicklungen des Web Information Retrieval (IR), wie sie Google oder GS implementieren, werten insbesondere frei zugängliche Dokumente mit ihrer gesamten Text- und Linkinformation automatisch aus. Diese Verfahren sind vor allem deshalb erfolgreich, weil sie Ergebnislisten nach Relevanz gerankt darstellen, einfach und schnell zu recherchieren sind und direkt auf die Volltexte verweisen. Die qualitativen Verfahren der traditionellen Informationsanbieter (z. B. WoS) hingegen zeigen genau bei diesen Punkten (Ranking, Einfachheit und Volltextzugriff) Schwächen, überzeugen aber vor allem durch ihre Stringenz, in diesem Fall die selektive Aufnahme von qualitätsgeprüften Dokumenten in das System und die inhaltliche Erschließung der Dokumente (siehe dazu Mayr und Petras, 2008).
  19. Großjohann, K.: Gathering-, Harvesting-, Suchmaschinen (1996) 0.02 
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    Date
    7. 2.1996 22:38:41
    Pages
    22 S
  20. Höfer, W.: Detektive im Web (1999) 0.02 
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    Date
    22. 8.1999 20:22:06

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