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Multi-Viewpoints Ontological Knowledge Representation: A Fuzzy Description Logics Based Approach

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Published:19 June 2018Publication History

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ABSTRACT

Description Logics (DLs) play a key role in the design of ontologies. An ontology is a formal description of important concepts in a particular domain. In practice, a set of a specific-domain applications use different representations of the same real world entity due to various viewpoints, context, and specific interest. In this paper, we are interested in the problem of representing an ontology in a heterogeneous domain by taking into consideration different viewpoints and different terminologies of various users, groups or even communities in the organisation. This type of ontology, called multi-viewpoints ontology, confers to the same universe of discourse, several partial descriptions, where each one is relative to a particular viewpoint. Moreover, these partial descriptions share at global level, fuzzy ontological elements allowing the representation of vague/imprecise knowledge between the various viewpoints. So, our goal is to propose a fuzzy multi-viewpoints ontology Web language, which is an extension of OWL-DL language, to allow the multi-viewpoints ontologies representation.

References

  1. Rodríguez, N. D., Cuéllar, M. P., Lilius, J., & Calvo-Flores, M. D. (2014). A fuzzy ontology for semantic modelling and recognition of human behaviour. Knowledge-Based Systems, 66, 46--60. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Straccia, U. (2013). Foundations of Fuzzy Logic and Semantic Web Languages. CRC Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Pérez, I. J., Wikström, R., Mezei, J., Carlsson, C., & Herrera-Viedma, E. (2013). A new consensus model for group decision making using fuzzy ontology. Soft Computing, 17(9), 1617--1627. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Hemam, M. (2018). "An extension of the ontology web language with multi-viewpoints and probabilistic reasoning". Int. Journal of Advanced Intelligence Paradigms, Inderscience Publishers Ltd. (In press) Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Antoniou, G. and Harmelen, F.V. (2004) Web Ontology Language: OWL, Presented at Handbook on Ontologies, pp.67--92.Google ScholarGoogle ScholarCross RefCross Ref
  6. Sirin, E., Parsia, B., Cuenca Grau, B., Kalyanpur, A., and Katz, Y. (2006). Pellet: A practical OWL-DL reasoner. In Journal of Web Semantics 5 (2), 51--53. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Tsarkov, D., and Horrocks, I. (2006). FaCT++ description logic reasoner: System description. in Proceedings of the Third International Joint Conference on Automated Reasoning, Vol. 4130 of Lecture Notes in Computer Science, 292 -297. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Hemam, M., Djezzar, M., and Boufaida, Z., (2017). Multi-viewpoint Ontological Representation of Composite Concepts: a Description Logics Based Approach. Int. Journal of Intelligent Information and Database Systems, Inderscience Publishers Ltd, 10( 1/2), 51--68. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Balley, S., Parent C., Spaccapieta S., (2004): Modeling Geographic Data with Multiple Representations. In journal Geographical Information science. 18, (4), 327--352.Google ScholarGoogle Scholar
  10. Benchikha, F., Boufaida, M., (2007): The Viewpoint Mechanism for Object-oriented Databases Modelling, Distribution and Evolution; Journal of Computing and Information Technology - CIT 15, 2007, 2, 95--110,Google ScholarGoogle Scholar
  11. Benslimane, D., Arara, A., Falquet, G., Maamar, Z., Thiran, P., Gargouri, F., (2006): Contextual Ontologies: Motivations, Challenges, and Solutions. In Fourth Biennial International Conference on Advances in Information Systems (Springer). Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Lynda Djakhdjakha, L., Hemam, M. and Boufaida, Z. (2014). Towards a Representation for Multi-Viewpoints Ontology Alignments". Int Journal of Metadata, Semantics and Ontologies, Inderscience Publishers Ltd, Vol. 9, N° 2, pp. 91--102. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Zadeh, L.A. (1965) Fuzzy sets. Information and Control, Vol. 8 No. 3, pp. 338--353.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Sanchez, D. and Tettamanzi, A. (2006) Fuzzy quantification in fuzzy description logics, in Sanchez, E. (Ed.): Fuzzy Logic and the Semantic Web, Capturing Intelligence.Google ScholarGoogle ScholarCross RefCross Ref
  15. Stoilos, G. and Stamou, G. (2007) Extending fuzzy description logics for the semantic web, in Proceedings of the 3rd International Workshop on OWL: Experiences and Directions.Google ScholarGoogle Scholar
  16. Bobillo, F. and Straccia, U. (2011) Fuzzy ontology representation using OWL 2. in International Journal of Approximate Reasoning, Vol. 52(7), 1073--1094. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Kaufmann, A. (1975) Theory of fuzzy subsets. Academic Press, London.Google ScholarGoogle Scholar

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      • Published in

        cover image ACM Other conferences
        ICEMIS '18: Proceedings of the Fourth International Conference on Engineering & MIS 2018
        June 2018
        452 pages
        ISBN:9781450363921
        DOI:10.1145/3234698

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        Publication History

        • Published: 19 June 2018

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        ICEMIS '18 Paper Acceptance Rate73of200submissions,37%Overall Acceptance Rate215of605submissions,36%

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