Automated semantic ontology construction for foresight studies using large language models
DOI:
https://doi.org/10.20535/SRIT.2308-8893.2026.2.09Keywords:
foresight, large language models, semantic ontology, scenario analysis, weak signals, hierarchical clusteringAbstract
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.
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