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DOF: a generic approach of domain ontology fuzzification

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Abstract

Although recent studies on the Semantic Web have focused on crisp ontologies and knowledge representation, they have paid less attention to imprecise knowledge. However, the results of these studies constitute a Semantic Web that can answer requests almost perfectly with respect to precision. Nevertheless, they ensure low recall. As such, we propose in this research work a new generic approach of fuzzification that which allows a semantic representation of crisp and fuzzy data in a domain ontology. In the framework of our real case study, the obtained illustrate that our approach is highly better than the crisp one in terms of completeness, comprehensiveness, generality, comprehension and shareability.

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References

  1. Berners-Lee T, Hendler J, Lassila O. The Semantic Web. Scientific American, 2001, 284(5): 34–43

    Article  Google Scholar 

  2. Akremi H, Zghal S, Jouhet V, Diallo G. Fonto: une nouvelle méthode de la fuzzification d’ontologies. In: Proceedings of 6ièmes Journées Francophone sur les Ontologies. 2016, 111–122

  3. Lukasiewicz T, Straccia U. Managing uncertainty and vagueness in description logics for the Semantic Web. Journal of Web Semantics, 2008, 6(4): 291–308

    Article  Google Scholar 

  4. Straccia U. Reasoning with fuzzy description logics. Journal of Artificial Intelligence Research, 2001, 14: 137–166

    Article  MathSciNet  Google Scholar 

  5. Bobillo F, Straccia U. Fuzzy ontology representation using OWL 2. International Journal of Approximate Reasoning, 2011, 52(7): 1073–1094

    Article  MathSciNet  Google Scholar 

  6. Horrocks I, Kutz O, Sattler U. The even more irresistible SROIQ. In: Proceedings of the 10th International Conference on Principles of Knowledge Representation and Reasoning. 2006, 57–67

  7. Zekri F, Turki E, Bouaziz R. Alzfuzzyonto: une ontologie floue pour l’aide à la décision dans le domaine de la maladie d’alzheimer. In: Proceedings of Actes du 18ème Congrés INFORSID. 2015, 83–98

  8. Ghorbel H, Bahri A, Bouaziz R. A framework for fuzzy ontology models. In: Proceedings of Journées Francophones sur les Ontologies. 2008, 21–30

  9. Ghorbel H, Bahri A, Bouaziz R. Fuzzy ontologies model for Semantic Web. In: Proceedings of the 2nd International Conference on Information and Knowledge Management, eKNow. 2010

  10. Zhai J, Liang Y, Jiang J, Yu Y. Fuzzy ontology models based on fuzzy linguistic variable for knowledge management and information retrieval. In: Proceedings of International Conference on Intelligent Information Processing. 2008, 58–67

  11. Gomez-Romero J, Bobillo F, Ros M, Molina-Solana M, Ruiz M D, Martín-Bautista M J. A fuzzy extension of the semantic Building Information Model. Automation in Construction, 2015, 57: 202–212

    Article  Google Scholar 

  12. Zadeh L A. Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1999, 100(1): 9–34

    Article  MathSciNet  Google Scholar 

  13. Li X, Martínez J, Rubio G. A new fuzzy ontology development methodology (FOSM) proposal. IEEE Access, 2016, 4: 7111–7124

    Article  Google Scholar 

  14. Zadeh L A. A fuzzy-algorithmic approach to the definition of complex or imprecise concepts. International Journal of Man-Machine Studies, 1976, 8(3): 249–291

    Article  MathSciNet  Google Scholar 

  15. Molinera J A M, Gálvez I J P, Wikstrom R, Viedma E H, Carlsson C. Designing a decision support system for recommending smartphones using fuzzy ontologies. In: Proceedings of IEEE Intelligent Systems. 2014, 323–334

  16. Thiessard F, Mougin F, Diallo G, Jouhet V, Cossin S, Garcelon N, Campillo-Gimenez B, Jouini W, Grosjean J, Massari P, Griffon N, Dupuch M, Tayalati F, Dugas E, Balvet A, Grabar N, Pereira S, Frandji B, Darmoni S, Cuggia M. RAVEL: retrieval and visualization in electronic health records. In: Mantas J, et al., eds. Quality of Life through Quality of Information. Proceedings of MIE2012. IOS Press, 2012, 194–198

  17. Papadopoulos G T, Mylonas P, Mezaris V, Avrithis Y, Kompatsiaris I. Knowledge-assisted image analysis based on context and spatial optimization. International Journal on Semantic Web and Information Systems, 2006, 2(3): 17–36

    Article  Google Scholar 

  18. Diallo G. An effective method of large scale ontology matching. Journal of Biomedical Semantics, 2014, 5(1): 44

    Article  Google Scholar 

  19. Gruber T R. Ontology. In: Ling L, Tamer Özsu M, eds. The Encyclopedia of Database Systems. 2009, 1963–1965

  20. Sanchez E, Toro C, Carrasco E, Bonachela P, Parra C, Bueno G, Guijarro F. A knowledge-based clinical decision support system for the diagnosis of alzheimer disease. In: Proceedings of the 13th IEEE International Conference on e-Health Networking Applications and Services. 2011, 355–361

  21. Rodríguez N D, Cadahía O L, Cuéllar M P, Lilius J, Calvo-Flores M D. A fuzzy ontology for semantic modelling and recognition of human behaviour. Knowledge-Based Systems, 2014, 66: 46–60

    Article  Google Scholar 

  22. El-Sappagh S, Elmogy M, Riad A M. A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis. Artificial Intelligence in Medicine, 2015, 65(3): 179–208

    Article  Google Scholar 

  23. Quan T, Hui S C, Fong A C M. Automatic fuzzy ontology generation for semantic help-desk support. IEEE Transactions on Industrial Informatics, 2006, 2(3): 155–164

    Article  Google Scholar 

  24. Alexopoulos P, Wallace M, Kafentzis K, Askounis D. Ikarus-onto: a methodology to develop fuzzy ontologies from crisp ones. Knowledge and Information Systems, 2012, 32(3): 667–695

    Article  Google Scholar 

  25. Lukasiewicz J. A numerical interpretation of the theory of proposisiton (polish). In: Proceedings of Ruch Filozoficzny. 1970, 129–130

  26. Zhang F, Cheng J, Ma Z. A survey on fuzzy ontologies for the Semantic Web. The Knowledge Engineering Review, 2016, 31(3): 278–321

    Article  Google Scholar 

  27. Bobillo F, Straccia U. FuzzyDL: an expressive fuzzy description logic reasoner. In: Proceedings of International Conference on Fuzzy Systems. 2008, 923–930

  28. Bonissone P, Bouchon-Meunier B. Introduction to the special issue in memoriam of Lotfi A. Zadeh [Guest editorial]. IEEE Computational Intelligence Magazine, 2019, 14(1): 13–14

    Article  Google Scholar 

  29. Khan A, Doucette J A, Cohen R, Lizotte D J. Integrating machine learning into a medical decision support system to address the problem of missing patient data. In: Proceedings of the 11th International Conference on Machine Learning and Applications. 2012, 454–457

  30. Akremi H, Zghal S, Diallo G. Modeling of uncertainty: fuzzification of medical ontology. In: Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics. 2016, 1–4

  31. Edkins A, Cushley W. The jekyll and hyde nature of antibodies. Biological Sciences Review, 2012, 25(2): 2–5

    Google Scholar 

  32. Civili C. Query answering over ontologies specified via database dependencies. In: Proceedings of SIGMOD PhD Symposium. 2014, 36–40

  33. Djedidi R, Aufaure M. Onto-evoal an ontology evolution approach guided by pattern modeling and quality evaluation. In: Proceedings of International Symposium on Foundations of Information and Knowledge Systems. 2010, 286–305

  34. Alexopoulos P, Mylonas P. Towards vagueness-oriented quality assessment of ontologies. In: Proceedings of the 8th Hellenic Conference on Artificial Intelligence. 2014, 448–453

  35. Li G, Yan L, Ma Z. An approach for approximate subgraph matching in fuzzy rdf graph. Fuzzy Sets and Systems, 2019, 376: 106–126

    Article  MathSciNet  Google Scholar 

  36. Plebani M, Aita A, Padoan A, Sciacovelli L. Decision support and patient safety. Clinics in Laboratory Medicine, 2019, 39(2): 231–244

    Article  Google Scholar 

  37. Mohanta J C, Keshari A. A knowledge based fuzzy-probabilistic roadmap method for mobile robot navigation. Applied Soft Computing, 2019, 79: 391–409

    Article  Google Scholar 

  38. Lee C S, Jian W, Huang L K. A fuzzy ontology and its application to news summarization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2005, 35(5): 859–880

    Article  Google Scholar 

  39. Sani S, Aris T N M. Proposal for ontology based approach to fuzzy student model design. In: Proceedings of International Conference on Intelligent Systems, Modelling and Simulation. 2014, 35–37

  40. Truong H B, Quach X H. An overview of fuzzy ontology integration methods based on consensus theory. In: van Do T, Thi H, Nguyen N, eds. Advanced Computational Methods for Knowledge Engineering. Advances in Intelligent Systems and Computing. Springer, Cham, 2014, 217–227

    Chapter  Google Scholar 

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Acknowledgements

The work described in this paper was supported by the LIPAH Laboratory (LIPAH-LR 11ES14) of University of Tunis El Manar, Faculty of Sciences of Tunis (Tunisia). The authors wish to thank the anonymous referees for their valuable comments and suggestions.

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Authors and Affiliations

  1. Faculty of Sciences of Tunis, University of Tunis El Manar, Tunis, 2092, Tunisia

    Houda Akremi & Sami Zghal

  2. Faculté des Sciences Juridiques, Université de Jendouba, Jendouba, 8189, Tunisia

    Sami Zghal

Authors
  1. Houda Akremi
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  2. Sami Zghal
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Corresponding author

Correspondence to Houda Akremi.

Additional information

Houda Akremi is a PhD student in computer sciences at University of Tunis El-Manar, Tunisia. She earned a master’s degree in the Faculty of Sciences in Laws, Economics, and Management, in 2013. Her current research interests include and ontology management, and Semantic Web. She is currently a member of LIPAH Laboratory.

Sami Zghal received the PhD degree in computer sciences from the University of Artois, France, and University of Tunis El-Manar, Tunisia, in 2010. His experience in teaching, in computer science and information systems is around 20 years. He is an assistant professor with University of Jendouba, Tunisia, in the Faculty of Sciences in Laws, Economics and Management (computer sciences department). His research interests mainly focus on data representation and ontology management. He is also member LIPAH Laboratory.

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Akremi, H., Zghal, S. DOF: a generic approach of domain ontology fuzzification. Front. Comput. Sci. 15, 153322 (2021). https://doi.org/10.1007/s11704-020-9354-z

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  • DOI: https://doi.org/10.1007/s11704-020-9354-z

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