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Fuzzy Models

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  • First Online:
Encyclopedia of Database Systems

  • 56 Accesses

Synonyms

Aggregation operators; Flexible query languages; Fuzzy information retrieval

Definition

The application of fuzzy set theory to information retrieval (IR) is aimed at the definition of retrieval techniques capable of modeling, at least to some extent, the subjectivity, vagueness, and imprecision that is intrinsic to the process of locating information relevant to users’ needs. In the context of IR, fuzzy set theory has been applied to several purposes among which:

  • The definition of generalizations of the Boolean retrieval model and in particular the definition of flexible query languages to address the vagueness that may affect query formulation

  • The definition of flexible approaches to XML retrieval

  • The definition of flexible and personalized indexing algorithms

  • The definition of fuzzy thesauri and fuzzy clustering algorithms, which are often employed to extend the functionalities of a basic information retrieval system

  • The definition of flexible aggregation strategies of...

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Recommended Reading

  1. Bordogna G, Pasi G. Modelling vagueness in information retrieval. In: Agosti M, Crestani F, Pasi G, editors. Lectures in information retrieval. Berlin: Springer; 2001.

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  2. Bordogna G, Pasi G. Personalized indexing and retrieval of heterogeneous structured documents. Inf Retrieval. 2005;8(2):301–18.

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  3. Bordogna G, Pasi G, Yager RR. Soft approaches to distributed information retrieval. Int J Approx Reasoning. 2003;34(2–3):105–20.

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  4. da Costa Pereira C, Dragoni M, Pasi G. Multidimensional relevance: prioritized aggregation in a personalized information retrieval setting. Inf Process Manage. 2012;48(2):340–57.

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  5. Miyamoto S. Fuzzy sets in information retrieval and cluster analysis. Dordrecht: Kluwer Academic; 1990.

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  6. Panzeri E, Pasi G. An approach to define flexible structural constraints in XQuery. AMT. 2012:307–17.

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  7. Panzeri E, Pasi G. Flex-BaseX: an XML engine with a flexible extension of Xquery full-text. ACM SIGIR. 2013:1083–4.

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  8. Pasi G. Fuzzy sets in information retrieval: state of the art and research trends. In: Bustince H, Herrera F, Montero J, editors. Fuzzy sets and their extensions: representation, aggregation and models. Intelligent systems from decision making to data mining, web intelligence and computer vision, series: studies in fuzziness and soft computing. Berlin: Springer; 2008. p. 517–35.

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  9. Radecki T. Fuzzy set theoretical approach to document retrieval. Inf Process Manag. 1979;15(5):247–60.

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  10. Zadeh L. Fuzzy sets. Inf Control. 1965;8:338–53.

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Author information

Authors and Affiliations

  1. Department of Informatics, Systems and Communication, University of Milano-Bicocca, 20126, Milan, Italy

    Gabriella Pasi

Authors
  1. Gabriella Pasi
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Corresponding author

Correspondence to Gabriella Pasi .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, Georgia, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, Ontario, Canada

    M. Tamer Özsu

Section Editor information

  1. Fondazione Ugo Bordoni, Via B. Castiglione, 59, Rome, Italy

    Giambattista Amati

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Pasi, G. (2017). Fuzzy Models. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7993-3_925-2

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  • DOI: https://doi.org/10.1007/978-1-4899-7993-3_925-2

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4899-7993-3

  • Online ISBN: 978-1-4899-7993-3

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

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