Search (51 results, page 3 of 3)

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  1. Mansourian, Y.: Contextual elements and conceptual components of information visibility on the web (2008) 0.01
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    Date
    1. 1.2009 10:22:40
  2. Aloteibi, S.; Sanderson, M.: Analyzing geographic query reformulation : an exploratory study (2014) 0.01
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    Date
    26. 1.2014 18:48:22
  3. Monchaux, S.; Amadieu, F.; Chevalier, A.; Mariné, C.: Query strategies during information searching : effects of prior domain knowledge and complexity of the information problems to be solved (2015) 0.01
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    Date
    25. 1.2016 18:46:22
  4. 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
  5. 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
  6. 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
  7. Barrio, P.; Gravano, L.: Sampling strategies for information extraction over the deep web (2017) 0.01
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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.
  8. Toms, E.G.: What motivates the browser? (1999) 0.01
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    Date
    22. 3.2002 9:44:47
  9. Ennis, M.; Sutcliffe, A.G.; Watkinson, S.J.: Towards a predictive model of information seeking : empirical studies of end-user-searching (1999) 0.01
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    Date
    22. 3.2002 9:54:13
  10. Drabenstott, K.M.: Web search strategies (2000) 0.01
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    Date
    22. 9.1997 19:16:05
  11. Renugadevi, S.; Geetha, T.V.; Gayathiri, R.L.; Prathyusha, S.; Kaviya, T.: Collaborative search using an implicitly formed academic network (2014) 0.01
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    Date
    20. 1.2015 18:30:22

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