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International Conference on Soft Methods in Probability and Statistics

SMPS 2016: Soft Methods for Data Science pp 45–52Cite as

Representing Lightweight Ontologies in a Product-Based Possibility Theory Framework

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 456))

Abstract

This paper investigates an extension of lightweight ontologies, encoded here in DL-Lite languages , to the product-based possibility theory framework. We first introduce the language (and its associated semantics) used for representing uncertainty in lightweight ontologies. We show that, contrarily to a min-based possibilistic DL-Lite, query answering in a product-based possibility theory is a hard task. We provide equivalent transformations between the problem of computing an inconsistency degree (the key notion in reasoning from a possibilistic DL-Lite knowledge base) and the weighted maximum 2-Horn SAT problem.

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Acknowledgments

This work has been supported from the european project H2020 Marie Sklodowska-Curie Actions (MSCA) research and Innovation Staff Exchange (RISE): AniAge (High Dimensional Heterogeneous Data based Animation Techniques for Southeast Asian Intangible Cultural Heritage. This work has also been supported by the French National Research Agency ASPIQ project ANR-12-BS02-0003.

Author information

Authors and Affiliations

  1. CRIL - CNRS UMR 8188, University of Artois, 62307, Lens, France

    Salem Benferhat & Khaoula Boutouhami

  2. RIIMA, University of Sciences and Technology Houari Boumediene, Bab Ezzouar, Algeria

    Khaoula Boutouhami & Faiza Khellaf

  3. Aix-Marseille University, CNRS, ENSAM, University of Toulon, LSIS UMR 7296, Marseille, France

    Farid Nouioua

Authors
  1. Salem Benferhat
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  2. Khaoula Boutouhami
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  3. Faiza Khellaf
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  4. Farid Nouioua
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Corresponding author

Correspondence to Farid Nouioua .

Editor information

Editors and Affiliations

  1. Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy

    Maria Brigida Ferraro

  2. Department of Statistical Sciences, Sapienza University of Rome, Roma, Italy

    Paolo Giordani

  3. Dept of Basic & Applied Sciences Engg, Sapienza University of Rome, Rome, Italy

    Barbara Vantaggi

  4. Dept of Stoc Metds,Polish Academy of Sci, Systems Research Institute, Warsaw, Poland

    Marek Gagolewski

  5. Dept of Statis&OR and MD, Univ de Oviedo, Oviedo, Spain

    María Ángeles Gil

  6. Dept of Stoc Metds,Polish Aca of Science, Systems Res Inst, Warsaw, Poland

    Przemysław Grzegorzewski

  7. Sys Res Inti, Dept of Stoch Methods, Polish Acadmy Sci in Warsaw, Warsaw, Poland

    Olgierd Hryniewicz

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© 2017 Springer International Publishing Switzerland

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Benferhat, S., Boutouhami, K., Khellaf, F., Nouioua, F. (2017). Representing Lightweight Ontologies in a Product-Based Possibility Theory Framework. In: Ferraro, M., et al. Soft Methods for Data Science. SMPS 2016. Advances in Intelligent Systems and Computing, vol 456. Springer, Cham. https://doi.org/10.1007/978-3-319-42972-4_6

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  • DOI: https://doi.org/10.1007/978-3-319-42972-4_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42971-7

  • Online ISBN: 978-3-319-42972-4

  • eBook Packages: EngineeringEngineering (R0)

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