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  • × theme_ss:"Suchtaktik"
  1. Pontis, S.; Blandford, A.; Greifeneder, E.; Attalla, H.; Neal, D.: Keeping up to date : an academic researcher's information journey (2017) 0.01 
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    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.1, S.22-35
  2. Sachse, J.: ¬The influence of snippet length on user behavior in mobile web search (2019) 0.01 
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    Date
    20. 1.2015 18:30:22
  3. Hopkins, M.E.; Zavalina, O.L.: Evaluating physicians' serendipitous knowledge discovery in online discovery systems : a new approach (2019) 0.01 
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    Date
    20. 1.2015 18:30:22
  4. Ford, N.; Wood, F.; Walsh, C.: Cognitive styles and searching (1994) 0.00 
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  5. Pu, H.-T.; Chuang, S.-L.; Yang, C.: Subject categorization of query terms for exploring Web users' search interests (2002) 0.00 
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  6. Kellar, M.; Watters, C.; Shepherd, M.: ¬A field study characterizing Web-based information seeking tasks (2007) 0.00 
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  7. Spink, A.; Danby, S.; Mallan, K.; Butler, C.: Exploring young children's web searching and technoliteracy (2010) 0.00 
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  8. Agarwal, N.K.; Xu, Y.(C.); Poo, D.C.C.: ¬A context-based investigation into source use by information seekers (2011) 0.00 
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  9. Cole, C.: ¬A theory of information need for information retrieval that connects information to knowledge (2011) 0.00 
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  10. Grau, B.: Finding answers to questions, in text collections or Web, in open domain or specialty domains (2012) 0.00 
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    Source
    Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a
  11. Tamine, L.; Chouquet, C.: On the impact of domain expertise on query formulation, relevance assessment and retrieval performance in clinical settings (2017) 0.00 
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  12. Wang, Y.; Shah, C.: Authentic versus synthetic : an investigation of the influences of study settings and task configurations on search behaviors (2022) 0.00 
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  13. Barrio, P.; Gravano, L.: Sampling strategies for information extraction over the deep web (2017) 0.00 
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    Abstract
    Information extraction systems discover structured information in natural language text. Having information in structured form enables much richer querying and data mining than possible over the natural language text. However, information extraction is a computationally expensive task, and hence improving the efficiency of the extraction process over large text collections is of critical interest. In this paper, we focus on an especially valuable family of text collections, namely, the so-called deep-web text collections, whose contents are not crawlable and are only available via querying. Important steps for efficient information extraction over deep-web text collections (e.g., selecting the collections on which to focus the extraction effort, based on their contents; or learning which documents within these collections-and in which order-to process, based on their words and phrases) require having a representative document sample from each collection. These document samples have to be collected by querying the deep-web text collections, an expensive process that renders impractical the existing sampling approaches developed for other data scenarios. In this paper, we systematically study the space of query-based document sampling techniques for information extraction over the deep web. Specifically, we consider (i) alternative query execution schedules, which vary on how they account for the query effectiveness, and (ii) alternative document retrieval and processing schedules, which vary on how they distribute the extraction effort over documents. We report the results of the first large-scale experimental evaluation of sampling techniques for information extraction over the deep web. Our results show the merits and limitations of the alternative query execution and document retrieval and processing strategies, and provide a roadmap for addressing this critically important building block for efficient, scalable information extraction.
  14. Toms, E.G.: What motivates the browser? (1999) 0.00 
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    Date
    22. 3.2002 9:44:47
  15. Ennis, M.; Sutcliffe, A.G.; Watkinson, S.J.: Towards a predictive model of information seeking : empirical studies of end-user-searching (1999) 0.00 
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    Date
    22. 3.2002 9:54:13
  16. Drabenstott, K.M.: Web search strategies (2000) 0.00 
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    Date
    22. 9.1997 19:16:05
  17. Renugadevi, S.; Geetha, T.V.; Gayathiri, R.L.; Prathyusha, S.; Kaviya, T.: Collaborative search using an implicitly formed academic network (2014) 0.00 
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    Date
    20. 1.2015 18:30:22
  18. Waschatz, B.: Schmökern ist schwierig : Viele Uni-Bibliotheken ordnen ihre Bücher nicht - Tipps für eine erfolgreiche Suche (2010) 0.00 
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    Date
    3. 5.1997 8:44:22
  19. Case, D.O.: Looking for information : a survey on research on information seeking, needs, and behavior (2002) 0.00 
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    Footnote
    Weitere Rez. in: iwp 65(2014) H.2, S.143-144 (C. Wolff)

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