Е-Публикации
Технически университет - София

Abstract: Ontologies are powerful and expressive mechanism for knowledge representation to support artificial intelligence applications. In many practical domains it is natural to use vague, probabilistic or imprecise knowledge. In this research we will discuss approaches for combining crisp, probabilistic and vague theories for modelling and automated reasoning with knowledge in practical domains. We also propose a methodology for mixed modelling of crisp and fuzzy or probabilistic knowledge by ontologies.

References

1. N. Devadoss and S. Ramakrishnan, "Knowledge repre-sentation using fuzzy ontologies-a review. ", International Journal of Computer Science and Information Technologies, 6 (5), 2015, pp. 4304-4308.
2. R. N. Carvalho, "Probabilistic ontology: representation and modeling methodology. ", George Mason University, 2011.
3. R. N. Carvalho, K. B. Laskey, and P. C. Costa, " PR-OWL-a language for defining probabilistic ontologies. ", International Journal of Approximate Reasoning, 91, 2017, pp. 56-79.
4. R. Penaloza, "Introduction to probabilistic ontologies. ". Reasoning Web. Declarative Artificial Intelligence: 16th International Summer School, Oslo, Norway, June 24-26, 2020, Tutorial Lectures 16, 2020, pp. 1-35.
5. D. Poole, C. Smyth, and R. Sharma, "Semantic science: Ontologies, data and probabilistic theories. ", In International Workshop on Uncertainty Reasoning for the Semantic Web (pp. 26-40), 2005.
6. F. Zhang, J. Cheng, and Z. Ma, "A survey on fuzzy ontologies for the Semantic Web. ", The Knowledge Engineering Review, 31 (3), 2016, pp. 278-321.
7. A. F. D. Kana, and B. O. Akinkunmi, "Fuzzy Reasoning Procedure for Ontologies based on Rough Membership Approximation. ", Future Computing & Informatics Journal, 7 (1), 2022.
8. G. Stoilos, and G. Stamou, "Reasoning with fuzzy extensions of OWL and OWL 2. ", Knowledge and information systems, 40 (1), 2014, pp. 205-242.
9. G. Stoilos, G. Stamou, and J. Z. Pan, "Fuzzy extensions of OWL: Logical properties and reduction to fuzzy description logics. ", International journal of approximate reasoning, 51 (6), 2010, pp. 656-679.
10. F. Bobillo, and U. Straccia, "Fuzzy ontology representation using OWL 2. ", International journal of approximate reasoning, 52 (7), 2011, pp. 1073-1094.
11. F. Bobillo, M. Delgado, and J. Gomez-Romero, " DeLorean: A reasoner for fuzzy OWL 2. ", Expert Systems with Applications, 39 (1), 2012, pp. 258-272.
12. F. Bobillo, and U. Straccia, "The fuzzy ontology reasoner fuzzyDL", Knowledge-Based Systems, 95, 2016, pp. 12-34.
13. I. Huitzil, F. Bobillo, J. Gomez-Romero, and U. Straccia, "Fudge: Fuzzy ontology building with consensuated fuzzy datatypes. ", Fuzzy Sets and Systems, 401, 2020, pp. 91-112.
14. I. Riali, M. Fareh, and H. Bouarfa, " A Semantic Approach for Handling Probabilistic Knowledge of Fuzzy Ontologies. ", In ICEIS (1), 2019, pp. 407-414.
15. I. Riali, M. Fareh, M. Ibnaissa, M. Bellil, "A semantic-based approach for hepatitis C virus prediction and diagnosis using a fuzzy ontology and a fuzzy Bayesian network. ", Journal of Intelligent & Fuzzy Systems, 44 (2), 2023, pp. 2381-2395.
16. A. Jain, and C. Gupta, "Fuzzy logic in recommender systems. ", Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications, 2018, pp. 255-273.
17. F. Cena, L. Console, and F. Vernero, "Logical foundations of knowledge-based recommender systems: A unifying spectrum of alternatives. ", Information Sciences, 546, 2021, pp. 60-73.
18. C. S. Lee, Z. W. Jian, and L. Huang, "A fuzzy ontology and its application to news summarization, ", IEEE Transactions on Systems, Man and Cyber-netics, 35 (5), 2005, pp. 859-880.
19. K. Todorov, C. Hudelot, A. Popescu, and P. Geibel, "Fuzzy ontology alignmentusing background knowledge, ", International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 22 (1), 2014, pp. 75-112.
20. D. Celik Ertugrul, and A. Elci, "A survey on semanticized and personalized health recommender systems. ", Expert Systems, 37 (4), e12519., 2020.
21. C. S. Lee, M. H. Wang, and H. Hagras, "A type-2 fuzzy ontology and its application to personal diabetic diet recommendation", IEEE Transactions on 875 Fuzzy Systems 18 (2), 2010, pp. 374-395.
22. K. Abicht, "OWL Reasoners still useable in 2023",. arXiv preprint arXiv: 2309. 06888, 2023.
23. J. Gao, D. Li, H. Wu, Z. Liu, L. Li, Z. Zheng, "Uncertain Knowledge Reasoning Based on the Fuzzy Multi-Entity Bayesian Network. ", Computers, Materials & Continua, 61 (1), 2019

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