Search (204 results, page 1 of 11)

  • × theme_ss:"Suchmaschinen"
  1. Li, L.; Shang, Y.; Zhang, W.: Improvement of HITS-based algorithms on Web documents 0.07
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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. Winterschladen, S.; Gurevych, I.: ¬Die perfekte Suchmaschine : Forschungsgruppe entwickelt ein System, das artverwandte Begriffe finden soll (2006) 0.01
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    Content
    "KÖLNER STADT-ANZEIGER: Frau Gurevych, Sie entwickeln eine Suchmaschine der nächsten Generation? Wie kann man sich diese vorstellen? IRYNA GUREVYCH Jeder kennt die herkömmlichen Suchmaschinen wie Google, Yahoo oder Altavista. Diese sind aber nicht perfekt, weil sie nur nach dem Prinzip der Zeichenerkennung funktionieren. Das steigende Informationsbedürfnis können herkömmliche Suchmaschinen nicht befriedigen. KStA: Wieso nicht? GUREVYCH Nehmen wir mal ein konkretes Beispiel: Sie suchen bei Google nach einem Rezept für einen Kuchen, der aber kein Obst enthalten soll. Keine Suchmaschine der Welt kann bisher sinnvoll solche oder ähnliche Anfragen ausführen. Meistens kommen Tausende von Ergebnissen, in denen der Nutzer die relevanten Informationen wie eine Nadel im Heuhaufen suchen muss. KStA: Und Sie können dieses Problem lösen? GUREVYCH Wir entwickeln eine Suchmaschine, die sich nicht nur auf das System der Zeichenerkennung verlässt, sondern auch linguistische Merkmale nutzt. Unsere Suchmaschine soll also auch artverwandte Begriffe zeigen. KStA: Wie weit sind Sie mit Ihrer Forschung? GUREVYCH Das Projekt ist auf zwei Jahre angelegt. Wir haben vor einem halben Jahr begonnen, haben also noch einen großen Teil vor uns. Trotzdem sind die ersten Zwischenergebnisse schon sehr beachtlich. KStA: Und wann geht die Suchmaschine ins Internet? GUREVYCH Da es sich um ein Projekt der Deutschen Forschungsgemeinschaft handelt, wird die Suchmaschine vorerst nicht veröffentlicht. Wir sehen es als unsere Aufgabe an, Verbesserungsmöglichkeiten durch schlaue Such-Algorithmen mit unseren Forschungsarbeiten nachzuweisen und Fehler der bekannten Suchmaschinen zu beseitigen. Und da sind wir auf einem guten Weg. KStA: Arbeiten Sie auch an einem ganz speziellen Projekt? GUREVYCH Ja, ihre erste Bewährungsprobe muss die neue Technologie auf einem auf den ersten Blick ungewöhnlichen Feld bestehen: Unsere Forschungsgruppe an der Technischen Universität Darmstadt entwickelt derzeit ein neuartiges System zur Unterstützung Jugendlicher bei der Berufsauswahl. Dazu stellt uns die Bundesagentur für Arbeit die Beschreibungen von 5800 Berufen in Deutschland zur Verfügung. KStA: Und was sollen Sie dann mit diesen konkreten Informationen machen? GUREVYCH Jugendliche sollen unsere Suchmaschine mit einem Aufsatz über ihre beruflichen Vorlieben flittern. Das System soll dann eine Suchabfrage starten und mögliche Berufe anhand des Interesses des Jugendlichen heraussuchen. Die persönliche Beratung durch die Bundesagentur für Arbeit kann dadurch auf alternative Angebote ausgeweitet werden. Ein erster Prototyp soll Ende des Jahres bereitstehen. KStA: Es geht also zunächst einmal nicht darum, einen Jobfür den Jugendlichen zu finden, sondern den perfekten Beruf für ihn zu ermitteln? GUREVYCH Ja, anhand der Beschreibung des Jugendlichen startet die Suchmaschine eine semantische Abfrage und sucht den passenden Beruf heraus. KStA: Gab es schon weitere Anfragen seitens der Industrie? GUREVYCH Nein, wir haben bisher noch keine Werbung betrieben. Meine Erfahrung zeigt, dass angesehene Kongresse die beste Plattform sind, um die Ergebnisse zu präsentieren und auf sich aufmerksam zu machen. Einige erste Veröffentlichungen sind bereits unterwegs und werden 2006 noch erscheinen. KStA: Wie sieht denn Ihrer Meinung nach die Suchmaschine der Zukunft aus? GUREVYCH Suchmaschinen werden immer spezieller. Das heißt, dass es etwa in der Medizin, bei den Krankenkassen oder im Sport eigene Suchmaschinen geben wird. Außerdem wird die Tendenz verstärkt zu linguistischen Suchmaschinen gehen, die nach artverwandten Begriffen fahnden. Die perfekte Suchmaschine wird wohl eine Kombination aus statistischem und linguistisch-semantischem Suchverhalten sein. Algorithmen, die wir am Fachgebiet Telekooperation an der TU Darmstadt entwickeln, werden für den nächsten qualitativen Sprung bei der Entwicklung der Suchmaschinen von größter Bedeutung sein."
  3. Gourbin, G.: ¬Une nouvelle profession : cyber-documentaliste l'exemple de Nomade (1998) 0.01
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    Date
    1. 8.1996 22:01:00
    Language
    f
    Location
    F
  4. Amato, G.; Rabitti, F.; Savino, P.: Multimedia document search on the Web (1998) 0.01
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    Date
    1. 8.1996 22:08:06
  5. Becker, F.: Internet-Suchmaschinen : Funktionsweise und Beurteilung (1999) 0.01
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    Date
    22. 3.2008 14:04:11
  6. Leighton, H.V.: Performance of four World Wide Web (WWW) index services : Infoseek, Lycos, WebCrawler and WWWWorm (1995) 0.00
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    Source
    http://www.winona.msus.edu/services-f/library-f/webind.htm
  7. Seiffert, F.: Wie indexieren Google & Co 13 Millionen Seiten? (2004) 0.00
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  8. Patalong, F.: Life after Google : III. Suchen und finden kann man auch ganz anders (2002) 0.00
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  9. Siering, F.: Barfuß zum Erfolg (1999) 0.00
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  10. Freistetter, F.: Vollkommen logisch (2020) 0.00
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  11. Eymard, G.; Francony, J.-M.: Vers un serveur d'indexation et de recherche sur le WEB (1999) 0.00
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    Language
    f
  12. Puscher, F.: Suchmaschinen im Härtetest (2001) 0.00
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  13. Heidenreich, F.: Know your search engine : a comparison of 6 popular Web search engines (1997) 0.00
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  14. Neumeier, F.: Verständnisprobleme : Internet Suchmaschinen (1998) 0.00
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  15. Broder, A.; Kumar, R.; Maghoul, F.; Raghavan, P.; Rajagopalan, S.; Stata, R.; Tomkins, A.; Wiener, J.: Graph structure in the Web (2000) 0.00
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  16. Sünkler, S.; Kerkmann, F.: How to do ... ? : Konzeption einer Spezialsuchmaschine für Tutorials (2016) 0.00
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  17. Wu, L.-L.; Chuang, Y.-L.; Chen, P.-Y.: Motivation for using search engines : a two-factor model (2008) 0.00
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    Abstract
    From a user-centered perspective, an effective search engine needs to attract new users to try out its features, and retain those users so that they continue using the features. In this article, we investigate the relations between users'motivation for using (i.e., trying out and continuing to use) a search engine and the engine's functional features. Based on Herzberg's two-factor theory (F. Herzberg, 2003; F. Herzberg, M. Bernard, & B. Snyderman, 1959), the features can be categorized as hygiene factors and motivation factors. Hygiene factors support the query process and provide a basic task context for information seeking that allows users to access relevant information. Motivation factors, on the other hand, help users navigate (i.e., browse) and comprehend the retrieved information, related to the task-content aspect of information seeking. Given the consistent findings that hygiene factors induce work motivation for a shorter period of time, it is hypothesized that hygiene factors are more effective in attracting users; while motivation factors are more effective in retaining than in attracting users. A survey, with 758 valid participants, was conducted to test the hypotheses. The empirical results provide substantial support for the proposed hypotheses and suggest that the two-factor theory can account for the motivation for using a search engine.
  18. Stalder, F.; Mayer, C.: ¬Der zweite Index : Suchmaschinen, Personalisierung und Überwachung (2009) 0.00
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    Source
    Deep Search: Politik des Suchens jenseits von Google; Deep Search-Konferenz, Wien, 2008.11.08; eine Veröffentlichung des World-Information Institute. Hrsg.: K. Becker u. F. Stalder
  19. Belbenoit-Avich, P.-M.: ¬Des phares dans la nuit (1996) 0.00
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  20. Duchemin, P.-Y.: ¬La recherche d'informations sur l'internet : repertoires et moteurs de recherche (1997) 0.00
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Years

Languages

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  • nl 1
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Types

  • a 180
  • el 19
  • m 8
  • p 2
  • s 2
  • x 2
  • r 1
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