Конвергенція моделей знань та моделей штучного інтелекту
The digitalization of various areas of activity to ensure sustainable development is accompanied by the increasing use of artificial intelligence (AI) tools. Adaptation of artificial intelligence models to the target application area can be carried out using subject knowledge models. The use of arti...
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| Datum: | 2025 |
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| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | Ukrainisch |
| Veröffentlicht: |
Kamianets-Podilskyi National Ivan Ohiienko University
2025
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| Online Zugang: | http://mcm-tech.kpnu.edu.ua/article/view/346303 |
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| Назва журналу: | Mathematical and computer modelling. Series: Technical sciences |
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Mathematical and computer modelling. Series: Technical sciences| Zusammenfassung: | The digitalization of various areas of activity to ensure sustainable development is accompanied by the increasing use of artificial intelligence (AI) tools. Adaptation of artificial intelligence models to the target application area can be carried out using subject knowledge models. The use of artificial intelligence in combination with effective knowledge management is crucial for ensuring the competitiveness of organizations in conditions of rapid environmental changes. The integration of artificial intelligence with knowledge models creates several problems related to the coordination of information processing models and the interpretation of their results. These problems are related to technological, organizational and ethical aspects. Large language models (LLM) based on deep learning (DL) methods are used in the field of natural language recognition (NLP). The convergence of LLM and GN aims to use the advantages of both models, providing a convergent model that can work well in both knowledge representation and logical inference. Applying knowledge models to classify AI applications in 5G/6G networks according to their role in network operations and impact on vertical areas such as the Internet of Things (IoT), healthcare, and transportation provides network optimization, predictive analytics, and improved security. The convergence of AI and knowledge models into a metaverse creates specific challenges that arise from the interaction between virtual environments and technologies. The article discusses approaches to ensuring the consistent use of AI and knowledge models in solving various tasks, and also identifies priority tasks related to the integration of AI and knowledge models |
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