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Modified Knowledge Inference Method Based on Fuzzy Ontology and Base of Cases

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Creativity in Intelligent Technologies and Data Science (CIT&DS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1084))

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Abstract

The article presents a formal ontological model of the rule set and the inference algorithm based on the analysis of the state of a complex technical system (local area network). Also, this paper presents the results of the integration of rules with fuzzy ontology for assessing the state of a local computer network in the process of artificially increasing traffic. In addition, the possibility of modifying this algorithm by means of a parallel launch of the mechanism for analyzing cases in the process of knowledge inference is considered. In conclusion, experiments with the Fuzzy OWL applied ontology prototype and the Pellet inference engine are presented.

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Acknowledgments

The study was supported by the Russian Foundation for Basic Research (Grants No. 19-07-00999, 18-37-00450 and 18-47-732007).

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Authors and Affiliations

  1. Ulyanovsk State Technical University, Ulyanovsk, Russia

    Vadim Moshkin & Nadezhda Yarushkina

Authors
  1. Vadim Moshkin
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  2. Nadezhda Yarushkina
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Corresponding author

Correspondence to Vadim Moshkin .

Editor information

Editors and Affiliations

  1. CAD & Research Department, Volgograd State Technical University, Volgograd, Russia

    Alla G. Kravets

  2. Electrical and Computer Engineering, University of Patras, Rion, Patras, Greece

    Peter P. Groumpos

  3. Volgograd State Technical University, Volgograd, Russia

    Maxim Shcherbakov

  4. Volgograd State Technical University, Volgograd, Russia

    Marina Kultsova

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Moshkin, V., Yarushkina, N. (2019). Modified Knowledge Inference Method Based on Fuzzy Ontology and Base of Cases. In: Kravets, A., Groumpos, P., Shcherbakov, M., Kultsova, M. (eds) Creativity in Intelligent Technologies and Data Science. CIT&DS 2019. Communications in Computer and Information Science, vol 1084. Springer, Cham. https://doi.org/10.1007/978-3-030-29750-3_8

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  • DOI: https://doi.org/10.1007/978-3-030-29750-3_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29749-7

  • Online ISBN: 978-3-030-29750-3

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