Monitoring Transformative Technological Convergence Through LLM-Extracted Semantic Entity Triple Graphs
This work addresses the problem of technology forecasting for researchers and policymakers by providing a scalable method, though it is incremental as it builds on existing LLM and graph-based techniques.
The authors tackled the challenge of forecasting transformative technologies by proposing a data-driven pipeline that uses LLMs to extract semantic triples from text and construct graphs to detect technological convergence patterns, validated on arXiv preprints and USPTO patents to identify both established and emerging patterns.
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 semantic triples from unstructured text and construct a large-scale graph of technology-related entities and relations. We introduce a new method for grouping semantically similar technology terms (noun stapling) and develop graph-based metrics to detect convergence signals. The pipeline includes multi-stage filtering, domain-specific keyword clustering, and a temporal trend analysis of topic co-occurence. We validate our methodology on two complementary datasets: 278,625 arXiv preprints (2017--2024) to capture early scientific signals, and 9,793 USPTO patent applications (2018-2024) to track downstream commercial developments. Our results demonstrate that the proposed pipeline can identify both established and emerging convergence patterns, offering a scalable and generalizable framework for technology forecasting grounded in full-text analysis.