Search (99 results, page 1 of 5)

  • × theme_ss:"Retrievalalgorithmen"
  1. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.04 
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    Source
    Information processing and management. 22(1986) no.6, S.465-476
  2. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.03 
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    Date
    14. 6.2015 22:12:44
    Source
    Deutscher Dokumentartag 1985, Nürnberg, 1.-4.10.1985: Fachinformation: Methodik - Management - Markt; neue Entwicklungen, Berufe, Produkte. Bearb.: H. Strohl-Goebel
  3. Baloh, P.; Desouza, K.C.; Hackney, R.: Contextualizing organizational interventions of knowledge management systems : a design science perspectiveA domain analysis (2012) 0.02 
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    Abstract
    We address how individuals' (workers) knowledge needs influence the design of knowledge management systems (KMS), enabling knowledge creation and utilization. It is evident that KMS technologies and activities are indiscriminately deployed in most organizations with little regard to the actual context of their adoption. Moreover, it is apparent that the extant literature pertaining to knowledge management projects is frequently deficient in identifying the variety of factors indicative for successful KMS. This presents an obvious business practice and research gap that requires a critical analysis of the necessary intervention that will actually improve how workers can leverage and form organization-wide knowledge. This research involved an extensive review of the literature, a grounded theory methodological approach and rigorous data collection and synthesis through an empirical case analysis (Parsons Brinckerhoff and Samsung). The contribution of this study is the formulation of a model for designing KMS based upon the design science paradigm, which aspires to create artifacts that are interdependent of people and organizations. The essential proposition is that KMS design and implementation must be contextualized in relation to knowledge needs and that these will differ for various organizational settings. The findings present valuable insights and further understanding of the way in which KMS design efforts should be focused.
    Date
    11. 6.2012 14:22:34
  4. Brenner, E.H.: Beyond Boolean : new approaches in information retrieval; the quest for intuitive online search systems past, present & future (1995) 0.02 
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    Abstract
    The challenge of effectively bringing specific, relevant information from the global sea of data to our fingertips, has become an increasingly difficult one. Discusses how the online information industry, founded on Boolean search systems, may be evolving to take advantage of other methods, such as 'term weighting', 'relevance ranking' and 'query by example'
  5. Witschel, H.F.: Global term weights in distributed environments (2008) 0.01 
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    Date
    1. 8.2008 9:44:22
    Source
    Information processing and management. 44(2008) no.3, S.1049-1061
  6. Liu, J.; Liu, C.: Personalization in text information retrieval : a survey (2020) 0.01 
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    Abstract
    Personalization of information retrieval (PIR) is aimed at tailoring a search toward individual users and user groups by taking account of additional information about users besides their queries. In the past two decades or so, PIR has received extensive attention in both academia and industry. This article surveys the literature of personalization in text retrieval, following a framework for aspects or factors that can be used for personalization. The framework consists of additional information about users that can be explicitly obtained by asking users for their preferences, or implicitly inferred from users' search behaviors. Users' characteristics and contextual factors such as tasks, time, location, etc., can be helpful for personalization. This article also addresses various issues including when to personalize, the evaluation of PIR, privacy, usability, etc. Based on the extensive review, challenges are discussed and directions for future effort are suggested.
  7. Ravana, S.D.; Rajagopal, P.; Balakrishnan, V.: Ranking retrieval systems using pseudo relevance judgments (2015) 0.01 
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    Date
    20. 1.2015 18:30:22
    18. 9.2018 18:22:56
    Source
    Aslib journal of information management. 67(2015) no.6, S.700-714
  8. Salton, G.: ¬A simple blueprint for automatic Boolean query processing (1988) 0.01 
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    Source
    Information processing and management. 24(1988) no.3, S.269-280
  9. Reddaway, S.: High speed text retrieval from large databases on a massively parallel processor (1991) 0.01 
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    Information processing and management. 27(1991), S.311-316
  10. Efthimiadis, E.N.: User choices : a new yardstick for the evaluation of ranking algorithms for interactive query expansion (1995) 0.01 
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    Date
    22. 2.1996 13:14:10
    Source
    Information processing and management. 31(1995) no.4, S.605-620
  11. Daniowicz, C.; Baliski, J.: Document ranking based upon Markov chains (2001) 0.01 
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    Information processing and management. 37(2001) no.4, S.623-637
  12. Horng, J.T.; Yeh, C.C.: Applying genetic algorithms to query optimization in document retrieval (2000) 0.01 
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    Information processing and management. 36(2000) no.5, S.737-759
  13. Ciocca, G.; Schettini, R.: ¬A relevance feedback mechanism for content-based image retrieval (1999) 0.01 
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    Information processing and management. 35(1999) no.5, S.605-632
  14. Niemi, T.; Junkkari, M.; Järvelin, K.; Viita, S.: Advanced query language for manipulating complex entities (2004) 0.01 
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    Information processing and management. 40(2004) no.6, S.869-
  15. Koyama, M.: ¬A fast retrieving algorithm of hierarchical relationships using tree structures (1998) 0.01 
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    Information processing and management. 34(1998) no.6, S.761-763
  16. Lee, J.H.: Combining the evidence of different relevance feedback methods for information retrieval (1998) 0.01 
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    Information processing and management. 34(1998) no.6, S.681-691
  17. Clarke, C.L.A.; Cormack, G.V.; Tudhope, E.A.: Relevance ranking for one to three term queries (2000) 0.01 
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    Information processing and management. 36(2000) no.2, S.291-311
  18. Chung, Y.M.; Lee, J.Y.: Optimization of some factors affecting the performance of query expansion (2004) 0.01 
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    Information processing and management. 40(2004) no.6, S.891-
  19. Salton, G.; Buckley, C.: Term-weighting approaches in automatic text retrieval (1988) 0.01 
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    Information processing and management. 24(1988) no.5, S.513-523
  20. Okada, M.; Ando, K.; Lee, S.S.; Hayashi, Y.; Aoe, J.I.: ¬An efficient substring search method by using delayed keyword extraction (2001) 0.01 
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    Information processing and management. 37(2001) no.5, S.741-761

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