Monitoring Transformative Technological Convergence Through LLM-Extracted Semantic Entity Triple Graphs
arXiv:2510.25370v2 Announce Type: replace Abstract: Forecasting transformative technologies remains a critical but challenging task, particularly in fast-evolving domains such as Information and Communication Technologies (ICTs). Traditional expert-based methods struggle to keep pace with short innovation cycles and ambiguous early-stage terminology. In this work, we propose a novel, data-driven pipeline to monitor the emergence of transformative technologies by identifying patterns of technological convergence. Our approach leverages advances in Large Language Models (LLMs) to extract semanti
The rapid advancement and adoption of large language models have created new capabilities for sophisticated text analysis, making such applications feasible and timely.
This work introduces a data-driven method for forecasting transformative technologies, moving beyond traditional expert-based approaches which struggle with the pace of innovation.
The ability to monitor technological convergence proactively using LLMs can provide policymakers and investors with earlier and more accurate insights into emerging tech trends.
- · Tech Investors
- · Government Futurists
- · Innovation Policy Makers
- · Strategic R&D Departments
- · Traditional Technology Foresight Consultancies
Automated tools will become increasingly central to technological forecasting and trend analysis.
Improved foresight capabilities could lead to more efficient capital allocation towards nascent but high-potential technologies.
Nations and companies with advanced LLM-driven foresight might gain a strategic advantage in developing and deploying future transformative technologies.
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