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Granular Rules and Rule Frames for Compact Knowledge Representation

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Foundations of Intelligent Systems (ISMIS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9384))

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Abstract

Efficient management of big Rule-Based Systems constitutes an important challenge for Knowledge Engineering. This paper presents an approach based on Granular Sets and Granular Relations. Granules of data replace numerous low-level items and allow for concise definition of constraints over a single attribute. Granular Relations are used for specification of preconditions of rules. A single Granular Rule can replace numerous rules with atomic preconditions. By analogy to Relational Databases, a complete Granular Rule Frame consists of Rule Scheme and Rule Specification. Such approach allows for efficient and concise specification of powerful rules at the conceptual level and makes analysis of rule set easier. The detailed specifications of Granular Rules are much more concise than in the case of atomic attribute values, but still allow for incorporating all necessary details.

A. Ligęza—AGH University of Science and Technology; Research Contract No. 18.18.120.859.

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Notes

  1. 1.

    If this is not the case, it is enough to create a separate, single element granule for any remaining residual element.

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Authors and Affiliations

  1. AGH University of Science and Technology, al. Mickiewicza 30, 30-059, Kraków, Poland

    Antoni Ligęza

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  1. Antoni Ligęza
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Correspondence to Antoni Ligęza .

Editor information

Editors and Affiliations

  1. Computer Science, University of Bari, Bari, Italy

    Floriana Esposito

  2. Enssat, Lannion, France

    Olivier Pivert

  3. LISI-UFR d'Informatique, Université Claude Bernard Lyon 1, Villeurbanne Cedex, France

    Mohand-Said Hacid

  4. University of North Carolina, CHARLOTTE, North Carolina, USA

    Zbigniew W. Rás

  5. Dipartimento di Informatica, Università degli Studi di Bari, Bari, Italy

    Stefano Ferilli

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Ligęza, A. (2015). Granular Rules and Rule Frames for Compact Knowledge Representation. In: Esposito, F., Pivert, O., Hacid, MS., Rás, Z., Ferilli, S. (eds) Foundations of Intelligent Systems. ISMIS 2015. Lecture Notes in Computer Science(), vol 9384. Springer, Cham. https://doi.org/10.1007/978-3-319-25252-0_23

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  • DOI: https://doi.org/10.1007/978-3-319-25252-0_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25251-3

  • Online ISBN: 978-3-319-25252-0

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