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Connective Comprehension: An individual differences study ...
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Considering Commonsense in Solving QA: Reading Comprehension with Semantic Search and Continual Learning
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4099 (2022)
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Re-Evaluating Early Memorization of the Qurʾān in Medieval Muslim Cultures
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In: Religions; Volume 13; Issue 2; Pages: 179 (2022)
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Knowledge Representation and Reasoning with an Extended Dynamic Uncertain Causality Graph under the Pythagorean Uncertain Linguistic Environment
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4670 (2022)
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Abstract:
A dynamic uncertain causality graph (DUCG) is a probabilistic graphical model for knowledge representation and reasoning, which has been widely used in many areas, such as probabilistic safety assessment, medical diagnosis, and fault diagnosis. However, the convention DUCG model fails to model experts’ knowledge precisely because knowledge parameters were crisp numbers or fuzzy numbers. In reality, domain experts tend to use linguistic terms to express their judgements due to professional limitations and information deficiency. To overcome the shortcomings of DUCGs, this article proposes a new type of DUCG model by integrating Pythagorean uncertain linguistic sets (PULSs) and the evaluation based on the distance from average solution (EDAS) method. In particular, experts express knowledge parameters in the form of the PULSs, which can depict the uncertainty and vagueness of expert knowledge. Furthermore, this model gathers the evaluations of experts on knowledge parameters and handles conflicting opinions among them. Moreover, a reasoning algorithm based on the EDAS method is proposed to improve the reliability and intelligence of expert systems. Lastly, an industrial example concerning the root cause analysis of abnormal aluminum electrolysis cell condition is provided to demonstrate the proposed DUCG model.
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Keyword:
dynamic uncertain causality graph (DUCG); evaluation based on distance from average solution (EDAS); expert system; knowledge representation and reasoning; Pythagorean uncertain linguistic set
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URL: https://doi.org/10.3390/app12094670
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A Deep Fusion Matching Network Semantic Reasoning Model
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In: Applied Sciences; Volume 12; Issue 7; Pages: 3416 (2022)
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OntoDomus: A Semantic Model for Ambient Assisted Living System Based on Smart Homes
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In: Electronics; Volume 11; Issue 7; Pages: 1143 (2022)
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Is Early Bilingual Experience Associated with Greater Fluid Intelligence in Adults?
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In: Languages; Volume 7; Issue 2; Pages: 100 (2022)
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Design of an Artificial Intelligence of Things Based Indoor Planting Model for Mentha Spicata
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In: Processes; Volume 10; Issue 1; Pages: 116 (2022)
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Formalization of AMR Inference via Hybrid Logic Tableaux ...
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Child Social Understanding: How Theory of Mind Development is Influenced by Socio-Cultural Factors
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Neural-based Knowledge Transfer in Natural Language Processing
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Ranking Semantics for Argumentation Systems With Necessities
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In: IJCAI 2020 - 29th International Joint Conference on Artificial Intelligence ; https://hal.archives-ouvertes.fr/hal-03002056 ; IJCAI 2020 - 29th International Joint Conference on Artificial Intelligence, Jan 2021, Yokohama / Virtual, Japan. pp.1912-1918, ⟨10.24963/ijcai.2020/265⟩ (2021)
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Ontological Formalisation of Mathematical Equations for Phenomic Data Exploitation
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In: The Semantic Web: ESWC 2021 Satellite Events ; https://hal.inrae.fr/hal-03408000 ; Ruben Verborgh; Anastasia Dimou; Aidan Hogan; Claudia d'Amato; Ilaria Tiddi; Arne Bröring; Simon Mayer; Femke Ongenae; Riccardo Tommasini; Mehwish Alam. The Semantic Web: ESWC 2021 Satellite Events, 12739, Springer International Publishing, pp.176-185, 2021, Lecture Notes in Computer Science, 978-3-030-80417-6. ⟨10.1007/978-3-030-80418-3_30⟩ (2021)
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Multimodal Conversation Modeling via Neural Perception, Structure Learning, and Communication
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How Do Language Intensity and Artificial Intelligence (AI) Affect Perceptions of Fact-checking Messages and Evaluations of Fact-checking Agencies?
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Xue, Haoning. - : eScholarship, University of California, 2021
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