Search (44 results, page 1 of 3)

  • × theme_ss:"Suchmaschinen"
  • × year_i:[2010 TO 2020}
  1. Souza, J.; Carvalho, A.; Cristo, M.; Moura, E.; Calado, P.; Chirita, P.-A.; Nejdl, W.: Using site-level connections to estimate link confidence (2012) 0.02
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  2. Sachse, J.: ¬The influence of snippet length on user behavior in mobile web search (2019) 0.02
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    Date
    20. 1.2015 18:30:22
  3. Bilal, D.: Ranking, relevance judgment, and precision of information retrieval on children's queries : evaluation of Google, Yahoo!, Bing, Yahoo! Kids, and ask Kids (2012) 0.01
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    Abstract
    This study employed benchmarking and intellectual relevance judgment in evaluating Google, Yahoo!, Bing, Yahoo! Kids, and Ask Kids on 30 queries that children formulated to find information for specific tasks. Retrieved hits on given queries were benchmarked to Google's and Yahoo! Kids' top-five ranked hits retrieved. Relevancy of hits was judged on a graded scale; precision was calculated using the precision-at-ten metric (P@10). Yahoo! and Bing produced a similar percentage in hit overlap with Google (nearly 30%), but differed in the ranking of hits. Ask Kids retrieved 11% in hit overlap with Google versus 3% by Yahoo! Kids. The engines retrieved 26 hits across query clusters that overlapped with Yahoo! Kids' top-five ranked hits. Precision (P) that the engines produced across the queries was P = 0.48 for relevant hits, and P = 0.28 for partially relevant hits. Precision by Ask Kids was P = 0.44 for relevant hits versus P = 0.21 by Yahoo! Kids. Bing produced the highest total precision (TP) of relevant hits (TP = 0.86) across the queries, and Yahoo! Kids yielded the lowest (TP = 0.47). Average precision (AP) of relevant hits was AP = 0.56 by leading engines versus AP = 0.29 by small engines. In contrast, average precision of partially relevant hits was AP = 0.83 by small engines versus AP = 0.33 by leading engines. Average precision of relevant hits across the engines was highest on two-word queries and lowest on one-word queries. Google performed best on natural language queries; Bing did the same (P = 0.69) on two-word queries. The findings have implications for search engine ranking algorithms, relevance theory, search engine design, research design, and information literacy.
  4. Next generation search engines : advanced models for information retrieval (2012) 0.01
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    Content
    Enthält die Beiträge: Das, A., A. Jain: Indexing the World Wide Web: the journey so far. Ke, W.: Decentralized search and the clustering paradox in large scale information networks. Roux, M.: Metadata for search engines: what can be learned from e-Sciences? Fluhr, C.: Crosslingual access to photo databases. Djioua, B., J.-P. Desclés u. M. Alrahabi: Searching and mining with semantic categories. Ghorbel, H., A. Bahri u. R. Bouaziz: Fuzzy ontologies building platform for Semantic Web: FOB platform. Lassalle, E., E. Lassalle: Semantic models in information retrieval. Berry, M.W., R. Esau u. B. Kiefer: The use of text mining techniques in electronic discovery for legal matters. Sleem-Amer, M., I. Bigorgne u. S. Brizard u.a.: Intelligent semantic search engines for opinion and sentiment mining. Hoeber, O.: Human-centred Web search.
    Vert, S.: Extensions of Web browsers useful to knowledge workers. Chen, L.-C.: Next generation search engine for the result clustering technology. Biskri, I., L. Rompré: Using association rules for query reformulation. Habernal, I., M. Konopík u. O. Rohlík: Question answering. Grau, B.: Finding answers to questions, in text collections or Web, in open domain or specialty domains. Berri, J., R. Benlamri: Context-aware mobile search engine. Bouidghaghen, O., L. Tamine: Spatio-temporal based personalization for mobile search. Chaudiron, S., M. Ihadjadene: Studying Web search engines from a user perspective: key concepts and main approaches. Karaman, F.: Artificial intelligence enabled search engines (AIESE) and the implications. Lewandowski, D.: A framework for evaluating the retrieval effectiveness of search engines.
  5. Cornelius, P.; Schneemann, R.; Weisel, L.: SUMA-Kongress 2011 - eine bemerkenswerte Tagung ! (2011) 0.01
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  6. Lewandowski, D.: Query understanding (2011) 0.01
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    Date
    18. 9.2018 18:22:18
  7. Bensman, S.J.: Eugene Garfield, Francis Narin, and PageRank : the theoretical bases of the Google search engine (2013) 0.01
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    Date
    17.12.2013 11:02:22
  8. Tober, M.; Hennig, L.; Furch, D.: SEO Ranking-Faktoren und Rang-Korrelationen 2014 : Google Deutschland (2014) 0.01
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    Date
    13. 9.2014 14:45:22
  9. Schaat, S.: Von der automatisierten Manipulation zur Manipulation der Automatisierung (2019) 0.01
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    Date
    19. 2.2019 17:22:00
  10. Schaer, P.; Mayr, P.; Sünkler, S.; Lewandowski, D.: How relevant is the long tail? : a relevance assessment study on million short (2016) 0.01
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  11. 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) 0.01
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  12. Thelwall, M.: Assessing web search engines : a webometric approach (2011) 0.01
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    Source
    Innovations in information retrieval: perspectives for theory and practice. Eds.: A. Foster, u. P. Rafferty
  13. Fluhr, C.: Crosslingual access to photo databases (2012) 0.01
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    Date
    17. 4.2012 14:25:22
  14. Chen, L.-C.: Next generation search engine for the result clustering technology (2012) 0.01
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    Date
    17. 4.2012 15:22:11
  15. Bouidghaghen, O.; Tamine, L.: Spatio-temporal based personalization for mobile search (2012) 0.01
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    Date
    20. 4.2012 13:19:22
  16. Lewandowski, D.: ¬Die Macht der Suchmaschinen und ihr Einfluss auf unsere Entscheidungen (2014) 0.01
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    Date
    22. 9.2014 18:54:11
  17. Huvila, I.: Affective capitalism of knowing and the society of search engine (2016) 0.01
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    Date
    20. 1.2015 18:30:22
  18. Knitel, M.; Labner, J.; Oberhauser, O.: Sachliche Erschließung und die neue Suchmaschine des Österreichischen Bibliothekenverbundes (2010) 0.01
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  19. Truran, M.; Schmakeit, J.-F.; Ashman, H.: ¬The effect of user intent on the stability of search engine results (2011) 0.01
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  20. Siebenlist, T.: MEMOSE. Spezialsuchmaschine für emotional geladene Dokumente (2012) 0.01
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    Content
    Vgl.: http://www.degruyter.com/view/j/iwp.2012.63.issue-4/iwp-2012-0050/iwp-2012-0050.xml?format=INT.

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  • d 17

Types

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  • el 7
  • m 3
  • r 1
  • s 1
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