DOF: a generic approach of domain ontology fuzzification

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.

Keywords

crisp ontology / fuzzy ontology / fuzzy logic / fuzzy reasoning / domain ontology

References

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

[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

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

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

[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

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

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

[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

[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

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

[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

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

[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

[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

[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

[27] Bobillo F, Straccia U. Fuzzy DL: 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

[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

[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

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

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

[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

[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

RIGHTS & PERMISSIONS

2020 Higher Education Press