Monitoring Transformative Technological Convergence Through LLM-Extracted Semantic Entity Triple Graphs

TL;DR

This paper presents a data-driven, LLM-based pipeline that extracts semantic entity graphs from scientific and patent texts to monitor and forecast transformative technological convergence.

Contribution

It introduces a novel method combining LLM-extracted semantic triples, noun stapling, and graph metrics to detect technological convergence signals from large-scale text datasets.

Findings

Successfully identified early scientific signals of convergence.

Tracked downstream commercial developments through patent analysis.

Demonstrated scalability and generalizability of the approach.

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 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.