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

2025-10-29

AI Summary

Rapid emergence of transformative technologies in domains such as ICT, coupled with ambiguous terminology and lagging expert-driven approaches, hinders timely identification of technology convergence. Method: We propose a dynamic technology convergence monitoring framework grounded in large language models (LLMs). It innovatively employs noun phrase concatenation to aggregate semantically similar terms, followed by multi-stage filtering, domain-specific keyword clustering, and temporal co-occurrence analysis to extract scientific semantic triples from full-text corpora and construct large-scale entity-relation graphs. The framework further integrates graph-theoretic metrics, thematic co-occurrence trends, and cross-source temporal validation—leveraging 270,000 arXiv papers and ~10,000 U.S. patents—to enable scalable detection and multi-stage forecasting of convergence patterns. Results: Experiments demonstrate that the framework effectively identifies both established and emerging technology convergence pathways, significantly improving accuracy and robustness in early-stage technology emergence monitoring.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Information Extraction
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Topic discovery and tracking

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.

Problem

• Monitoring technological convergence using LLM-extracted semantic triples
• Forecasting transformative technologies through data-driven entity graphs
• Detecting emerging convergence patterns from scientific and patent data

Innovation

• LLM-extracted semantic triples from text
• Graph-based metrics detect convergence signals
• Multi-stage filtering and keyword clustering