000150579 001__ 150579
000150579 005__ 20260112133353.0
000150579 0247_ $$2doi$$a10.1007/s40815-024-01796-y
000150579 0248_ $$2sideral$$a142661
000150579 037__ $$aART-2024-142661
000150579 041__ $$aeng
000150579 100__ $$aRiali, Ishak
000150579 245__ $$aProbFuzzOnto: A Fuzzy Ontology-Driven Uncertainty Approach Using Fuzzy Bayesian Networks
000150579 260__ $$c2024
000150579 5060_ $$aAccess copy available to the general public$$fUnrestricted
000150579 5203_ $$aThe need to deal with uncertain semantics is rising in importance in most of the important technology trends, and consequently, many proposals have emerged as solutions in recent years. Fuzzy ontologies were proposed to remedy the limitations of standard ontologies using fuzzy logic to deal with vague and imprecise knowledge. Nevertheless, fuzzy ontologies cannot deal with probabilistic knowledge which is an important characteristic of most real-world applications. This paper proposes a novel solution that aims at enhancing the knowledge representation and reasoning in fuzzy ontologies. Indeed, the proposed solution is a probabilistic extension of fuzzy ontologies with Fuzzy Bayesian Networks (FBN) that we named Probabilistic Fuzzy Ontologies (ProbFuzzOnto). It takes into account vague, imprecise, and probabilistic knowledge simultaneously. Moreover, this paper proposes a process to guide ontology engineers step by step in building ProbFuzzOnto. Also, it provides reasoning algorithms to drive implicit knowledge by utilizing explicit knowledge stored in a fuzzy ontology based on fuzzy Bayesian inference. To show the usefulness of the proposed solution, a case study in Renal Cancer is presented.
000150579 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2020-113903RB-I00$$9info:eu-repo/grantAgreement/ES/DGA/T42-23R
000150579 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000150579 590__ $$a3.6$$b2024
000150579 592__ $$a0.714$$b2024
000150579 591__ $$aAUTOMATION & CONTROL SYSTEMS$$b31 / 89 = 0.348$$c2024$$dQ2$$eT2
000150579 593__ $$aArtificial Intelligence$$c2024$$dQ2
000150579 591__ $$aCOMPUTER SCIENCE, INFORMATION SYSTEMS$$b93 / 258 = 0.36$$c2024$$dQ2$$eT2
000150579 593__ $$aComputational Theory and Mathematics$$c2024$$dQ2
000150579 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b80 / 204 = 0.392$$c2024$$dQ2$$eT2
000150579 593__ $$aTheoretical Computer Science$$c2024$$dQ2
000150579 593__ $$aInformation Systems$$c2024$$dQ2
000150579 593__ $$aSoftware$$c2024$$dQ2
000150579 593__ $$aControl and Systems Engineering$$c2024$$dQ2
000150579 594__ $$a7.7$$b2024
000150579 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000150579 700__ $$aFareh, Messaouda
000150579 700__ $$0(orcid)0000-0001-5136-4152$$aBobillo, Fernando$$uUniversidad de Zaragoza
000150579 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000150579 773__ $$g(2024), [21 pp.]$$pInternational Journal of Fuzzy Systems$$tInternational Journal of Fuzzy Systems$$x1562-2479
000150579 8564_ $$s2999164$$uhttps://zaguan.unizar.es/record/150579/files/texto_completo.pdf$$yVersión publicada
000150579 8564_ $$s2173498$$uhttps://zaguan.unizar.es/record/150579/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000150579 909CO $$ooai:zaguan.unizar.es:150579$$particulos$$pdriver
000150579 951__ $$a2026-01-12-13:20:05
000150579 980__ $$aARTICLE