Managing Group Decision Making criteria values using Fuzzy Ontologies

Morente-Molinera, Juan Antonio; Cabrerizo, Francisco Javier; Trillo, José Ramón; Pérez, Ignacio Javier; Herrera‐Viedma, Enrique

doi:10.1016/j.procs.2022.01.021

Cited by 12 publications

(4 citation statements)

References 21 publications

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“….l k is the set of all possible links between the taxonomic words a and b. Determine the length of this route by setting |path(a, b)| = k. The semantic distance dis(a, b) between a and b is thus defined as when all feasible routes between a and b are considered [21,22]:…”

Section: Fuzzy Ontologymentioning

confidence: 99%

“…Ontology and metadata imperfections should thus be considered. By avoiding imperfections and making assumptions about the quality of ontologies and metadata, we may develop new ontologies by showing how semantic information is used in the domain [19][20][21][22][23].…”

Section: Fuzzy Ontologymentioning

confidence: 99%

“…To enhance ontologies' capacity for representation and provide exact representation of imprecise data, a fuzzy ontology is utilized. An ontology that employs fuzzy logic to create a more intuitive representation of imprecise and vague knowledge for the purpose of facilitating reasoning about it is called a fuzzy ontology [22]. The idea of fuzzy relations is first introduced in fuzzy ontologies.…”

Section: Fuzzy Ontologymentioning

confidence: 99%

“…where n is the total number of ontology terms, and n 1 is the total number of valuable domain terms that were extracted from selected text documents but are not ontology terms; p stands for the value of probability of (sub) property of ontology element, which indicates the probability that this element locates in the correct position. The learning process is said to be functioning effectively when metrics are increasing, and poorly when they are dropping [22][23][24][25][26].…”

Section: Fuzzy Ontologymentioning

confidence: 99%

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An Automated English Essay Scoring Engine Based on Neutrosophic Ontology for Electronic Education Systems

2023

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Most educators agree that essays are the best way to evaluate students’ understanding, guide their studies, and track their growth as learners. Manually grading student essays is a tedious but necessary part of the learning process. Automated Essay Scoring (AES) provides a feasible approach to completing this process. Interest in this area of study has exploded in recent years owing to the difficulty of simultaneously improving the syntactic and semantic scores of an article. Ontology enables us to consider the semantic constraints of the actual world. However, there are several uncertainties and ambiguities that cannot be accounted for by standard ontologies. Numerous AES strategies based on fuzzy ontologies have been proposed in recent years to reduce the possibility of imprecise knowledge presentation. However, no known efforts have been made to utilize ontologies with a higher level of fuzzification in order to enhance the effectiveness of identifying semantic mistakes. This paper presents the first attempt to address this problem by developing a model for efficient grading of English essays using latent semantic analysis (LSA) and neutrosophic ontology. In this regard, the presented work integrates commonly used syntactic and semantic features to score the essay. The integration methodology is implemented through feature-level fusion. This integrated vector is used to check the coherence and cohesion of the essay. Furthermore, the role of neutrosophic ontology is investigated by adding neutrosophic membership functions to the crisp ontology to detect semantic errors and give feedback. Neutrosophic logic allows the explicit inclusion of degrees of truthfulness, falsity, and indeterminacy. According to the comparison with state-of-the-art AES methods, the results show that the proposed model significantly improves the accuracy of scoring the essay semantically and syntactically and is able to provide feedback.

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Section: Fuzzy Ontologymentioning

confidence: 99%

Section: Fuzzy Ontologymentioning

confidence: 99%

Section: Fuzzy Ontologymentioning

confidence: 99%

Section: Fuzzy Ontologymentioning

confidence: 99%

See 2 more Smart Citations

An Automated English Essay Scoring Engine Based on Neutrosophic Ontology for Electronic Education Systems

2023

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Some Trends in Fuzzy Decision Making

Trillo

Cabrerizo

Chiclana

et al. 2022

Advances in Intelligent Systems and Computing

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Decision making is a line of research that has been consolidating since its beginnings in the 1960s. The aim of this paper is to show the evolution and future challenges of this line of research, focusing especially on its evaluation and information methods. For this purpose, some issues and trends of the fuzzy decision system are presented. By doing so, it is possible to show which trend fuzzy decision systems will follow and the challenges that may arise.

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