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Großjohann, K.: Gathering-, Harvesting-, Suchmaschinen (1996)
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- Date
- 7. 2.1996 22:38:41
- Pages
- 22 S
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- p
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Wätjen, H.-J.: Mensch oder Maschine? : Auswahl und Erschließung vonm Informationsressourcen im Internet (1996)
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- 2. 2.1996 15:40:22
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Chakrabarti, S.; Dom, B.; Kumar, S.R.; Raghavan, P.; Rajagopalan, S.; Tomkins, A.; Kleinberg, J.M.; Gibson, D.: Neue Pfade durch den Internet-Dschungel : Die zweite Generation von Web-Suchmaschinen (1999)
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-
Berinstein, P.: Turning visual : image search engines on the Web (1998)
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- Source
- Online. 22(1998) no.3, S.37-38,40-42
-
Amato, G.; Rabitti, F.; Savino, P.: Multimedia document search on the Web (1998)
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- Date
- 1. 8.1996 22:08:06
-
Schüler, P.: Wertes Wissen : Knowledge Management vermeidet Datenfriedhöfe (2001)
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- Date
- 8.11.2001 19:58:22
-
Spree, U.; Feißt, N.; Lühr, A.; Piesztal, B.; Schroeder, N.; Wollschläger, P.: Semantic search : State-of-the-Art-Überblick zu semantischen Suchlösungen im WWW (2011)
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-
Wu, L.-L.; Chuang, Y.-L.; Chen, P.-Y.: Motivation for using search engines : a two-factor model (2008)
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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.
- Footnote
- Vgl.: Wu, L.-L., A. Chuang u. P.-Y. Chen: Correction to Wu, L., Chuang, A., & Chen, P. (2008). Motivation for using search engines: A two factor model. Journal of American Society for Information Science and Technology, 59(11), 1829-1840. In: Journal of the American Society for Information Science and Technology. 61(2010) no.1, S.214-216.
-
Hancock, B.: Subject-specific search engines : using the Harvest system to gather and maintain information on the Internet (1998)
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- Date
- 6. 3.1997 16:22:15
-
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)
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-
Nait-Baha, L.; Jackiewicz, A.; Djioua, B.; Laublet, P.: Query reformulation for information retrieval on the Web using the point of view methodology : preliminary results (2001)
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-
Mayr, P.; Walter, A.-K.: Abdeckung und Aktualität des Suchdienstes Google Scholar (2006)
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- Abstract
- Der Beitrag widmet sich dem neuen Google-Suchdienst Google Scholar. Die Suchmaschine, die ausschließlich wissenschaftliche Dokumente durchsuchen soll, wird mit ihren wichtigsten Funktionen beschrieben und anschließend einem empirischen Test unterzogen. Die durchgeführte Studie basiert auf drei Zeitschriftenlisten: Zeitschriften von Thomson Scientific, Open AccessZeitschriften des Verzeichnisses DOAJ und in der Fachdatenbank SOLIS ausgewertete sozialwissenschaftliche Zeitschriften. Die Abdeckung dieser Zeitschriften durch Google Scholar wurde per Abfrage der Zeitschriftentitel überprüft. Die Studie zeigt Defizite in der Abdeckung und Aktualität des Google Scholarlndex. Weiterhin macht die Studie deutlich, wer die wichtigsten Datenlieferanten für den neuen Suchdienst sind und welche wissenschaftlichen Informationsquellen im Index repräsentiert sind. Die Pluspunkte von Google Scholar liegen in seiner Einfachheit, seiner Suchgeschwindigkeit und letztendlich seiner Kostenfreiheit. Die Recherche in Fachdatenbanken kann Google Scholar trotz sichtbarer Potenziale (z. B. Zitationsanalyse) aber heute aufgrund mangelnder fachlicher Abdeckung und Transparenz nicht ersetzen.
-
Nicholson, S.; Sierra, T.; Eseryel, U.Y.; Park, J.-H.; Barkow, P.; Pozo, E.J.; Ward, J.: How much of it is real? : analysis of paid placement in Web search engine results (2006)
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- Date
- 22. 7.2006 16:32:57
-
Boldi, P.; Santini, M.; Vigna, S.: PageRank as a function of the damping factor (2005)
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- Date
- 16. 1.2016 10:22:28
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Baeza-Yates, R.; Boldi, P.; Castillo, C.: Generalizing PageRank : damping functions for linkbased ranking algorithms (2006)
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- 16. 1.2016 10:22:28
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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)
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- Date
- 2.12.2007 19:39:22
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Mayr, P.: Google Scholar als akademische Suchmaschine (2009)
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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).
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Li, L.; Shang, Y.; Zhang, W.: Improvement of HITS-based algorithms on Web documents
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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/.
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Höfer, W.: Detektive im Web (1999)
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- Date
- 22. 8.1999 20:22:06
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Rensman, J.: Blick ins Getriebe (1999)
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- Date
- 22. 8.1999 21:22:59
