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Hsieh-Yee, I.: Search tactics of Web users in searching for texts, graphics, known items and subjects : a search simulation study (1998)
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- Date
- 25.12.1998 19:22:31
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Kosmin, L.J.: Teaching Internet end-users effective search strategies across diversified databases (1992)
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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
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Hsieh-Yee, I.: Research on Web-search behavior (2001)
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Ford, N.; Miller, D.; Moss, N.: Web search strategies and human individual differences : cognitive and demographic factors, Internet attitudes, and approaches (2005)
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- Abstract
- The research reported here was an exploratory study that sought to discover the effects of human individual differences an Web search strategy. These differences consisted of (a) study approaches, (b) cognitive and demographic features, and (c) perceptions of and preferred approaches to Web-based information seeking. Sixtyeight master's students used AItaVista to search for information an three assigned search topics graded in terms of complexity. Five hundred seven search queries were factor analyzed to identify relationships between the individual difference variables and Boolean and best-match search strategies. A number of consistent patterns of relationship were found. As task complexity increased, a number of strategic shifts were also observed an the part of searchers possessing particular combinations of characteristics. A second article (published in this issue of JASIST; Ford, Miller, & Moss, 2005) presents a combined analyses of the data including a series of regression analyses.
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Mansourian, I.: Web search efficacy : definition and implementation (2008)
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Pu, H.-T.; Chuang, S.-L.; Yang, C.: Subject categorization of query terms for exploring Web users' search interests (2002)
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Kellar, M.; Watters, C.; Shepherd, M.: ¬A field study characterizing Web-based information seeking tasks (2007)
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Spink, A.; Danby, S.; Mallan, K.; Butler, C.: Exploring young children's web searching and technoliteracy (2010)
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Barrio, P.; Gravano, L.: Sampling strategies for information extraction over the deep web (2017)
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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.
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Drabenstott, K.M.: Web search strategies (2000)
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- Date
- 22. 9.1997 19:16:05
