A web application for reasoning on probabilistic description logics knowledge bases

Zese, Riccardo; Bellodi, Elena

doi:10.1002/spe.3212

Cited by 1 publication

(3 citation statements)

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“…In formal methods, description logic is a deductive reasoning based approach that relies on rigorous mathematical logic reasoning to enable strict semantic detection of models, and has wide applications in many fields. Gottlob Georg introduced the stability of describing logical semantics [17] ; Zese et al using Probability Description Logic Knowledge Base to Infer Web Applications [18] ; Horrocks et al use description logic SHIQ for individual reasoning [19] .…”

Section: Related Workmentioning

confidence: 99%

“…DL [6] is a formal method of knowledge representation framework used in artificial intelligence and computer science. It provides a formalism for expressing and reasoning about the concepts and relationships within a domain of interest.…”

Section: Requirement Model and Semantic Preparationsmentioning

confidence: 99%

“…This article uses formal methods (DL) to validate the fused adaptive learning requirement feature model (ALRFM). description logic (DL) [6] is a kind of formal method based on strict semantics and inference rules. By using description logic for verification, ensure that the pre fusion model and the fused model have the same semantics, and ensure the correctness of the modeling.…”

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confidence: 99%

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Method of adaptive learning feature model fusion and verification based on metamodels

Wang,

Zhu,

et al. 2024

Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024)

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As the advancement of computer technique and artificial intelligence technology, education based on computer technology has become a hot topic, and the wave of educational informatization is rising. Among them, adaptive learning has received widespread attention as a type of educational informatization technology. However, the current adaptive learning feature models use different semantics to describe each other, and the models lack meta model level explanations, resulting in a lack of further operability of the models and difficulty in integrating with each other. In order to solve these problems, our paper proposes a fusion and verification method for learning feature adaptive models based on meta models. Above all, an adaptive learning feature meta model is proposed, which defines the construction rules of the adaptive learning feature model at meta model level, so that different models can be fused using the same set of semantic rules. Then, at the meta model level, transform the learning model into a requirement model. Finally, using formal methods, validate the fused feature model, which can make sure of the semantic consistency between the previous and subsequent models. This article demonstrates the entire process through a model fusion experiment. The experimental results indicate that this method is correct and feasible in promoting feature model fusion without adaptive learning.

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Section: Related Workmentioning

confidence: 99%

Section: Requirement Model and Semantic Preparationsmentioning

confidence: 99%

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