Search (7 results, page 1 of 1)

  • × theme_ss:"Suchtaktik"
  • × theme_ss:"Internet"
  1. Hsieh-Yee, I.: Search tactics of Web users in searching for texts, graphics, known items and subjects : a search simulation study (1998) 0.04
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    Date
    25.12.1998 19:22:31
  2. Kosmin, L.J.: Teaching Internet end-users effective search strategies across diversified databases (1992) 0.01
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    Source
    Online information 92. Proc. of the 16th Int. Online Information Meeting, London, 8-10.12.1992. Ed. by David I. Raitt
  3. Steinhaus, I.: Online recherchieren : Ökonomische Wege zu Informationen (1997) 0.01
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  4. Hsieh-Yee, I.: Research on Web-search behavior (2001) 0.01
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  5. Mansourian, I.: Web search efficacy : definition and implementation (2008) 0.01
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  6. 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.
  7. Drabenstott, K.M.: Web search strategies (2000) 0.01
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    Date
    22. 9.1997 19:16:05