Автоматизована конструкція семантичної онтології для досліджень передбачення із використанням великих лінгвістичних моделей

• Дата: 2026
• Автори та афіліації: Serhii Lupenko — Opole University of Technology, Opole; Mykhailo Stoliar — National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv; Oleksandr Terentiev — National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv; Volodymyr Savastiyanov — National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv
• Ключові слова: keywords
• Автори: Lupenko, Serhii, Stoliar, Mykhailo, Terentiev, Oleksandr, Savastiyanov, Volodymyr
• Формат: Стаття
• Мова: Англійська
• Опубліковано: The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 2026
• Теми: передбачення; великі мовні моделі; семантична онтологія; сценарний аналіз; слабкі сигнали; ієрархічна кластеризація
• Онлайн доступ: https://journal.iasa.kpi.ua/article/view/365265
• Назва журналу: System research and information technologies

Recent advances in large language models (LLMs) enable the automated discovery of semantic structures and emerging signals within text streams, offering an opportunity to redesign foresight workflows into continuous, data-driven systems. This study aims to develop and validate an automated framework for extracting, structuring, and comparing semantic ontologies using LLMs. The paralyzed approach was used for data mining from social media platforms and filtering non-domain data. The key semantic elements, goals and hypernyms corresponded, were extracted using multiple LLM configurations, with a consensus mechanism to provide semantic reliability and minimize hallucination. The extracted elements were embedded in a high-dimensional vector space, clustered iteratively using cosine similarity, and merged hierarchically. Convergence process and structural stability were analyzed using the elbow criterion and similarity metrics. The Proposed approach provides a cost-efficient alternative to traditional expert-based foresight analysis. By integrating LLM-driven semantic extraction with quantitative clustering, it enables the identification of emerging trends, weak signals, and long-term thematic structures. The results highlight the potential of LLM-based semantic modeling as a foundation for automated foresight systems.