AI-Enhanced Multi-Dimensional Measurement of Technological Convergence through Heterogeneous Graph and Semantic Learning

2025-09-25

AI Summary

Measuring the depth and breadth of technological convergence—characterized by its multidimensionality and dynamism—has long posed significant methodological challenges. To address this, we propose the Technological Convergence Index (TCI), the first dual-dimensional quantitative framework jointly capturing convergence depth (i.e., cross-domain knowledge integration intensity) and breadth (i.e., technological diversity). Methodologically, we innovate by integrating a heterogeneous graph Transformer with Sentence-BERT to achieve semantic alignment between IPC textual descriptions and patent metadata; we then objectively weight dimensions using the Shannon diversity index and entropy weighting. Empirical validation demonstrates that TCI significantly outperforms existing metrics in explaining patent quality and withstands multiple robustness checks. This work establishes a reproducible, scalable measurement paradigm for monitoring technological convergence and evaluating innovation policy effectiveness.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Application Domains: Humanities & Computational Social Science
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Security and Privacy: Large-scale security measurements

Abstract

Technological convergence refers to the phenomenon where boundaries between technological areas and disciplines are increasingly blurred. It enables the integration of previously distinct domains and has become a mainstream trend in today's innovation process. However, accurately measuring technological convergence remains a persistent challenge due to its inherently multidimensional and evolving nature. This study designs an Technological Convergence Index (TCI) that comprehensively measures convergence along two fundamental dimensions: depth and breadth. For depth calculation, we use IPC textual descriptions as the analytical foundation and enhance this assessment by incorporating supplementary patent metadata into a heterogeneous graph structure. This graph is then modeled using Heterogeneous Graph Transformers in combination with Sentence-BERT, enabling a precise representation of knowledge integration across technological boundaries. Complementing this, the breadth dimension captures the diversity of technological fields involved, quantified through the Shannon Diversity Index to measure the variety of technological combinations within patents. Our final TCI is constructed using the Entropy Weight Method, which objectively assigns weights to both dimensions based on their information entropy. To validate our approach, we compare the proposed TCI against established convergence measures, demonstrating its comparative advantages. We further establish empirical reliability through a novel robustness test that regresses TCI against indicators of patent quality. These findings are further substantiated through comprehensive robustness checks. Our multidimensional approach provides valuable practical insights for innovation policy and industry strategies in managing emerging cross-domain technologies.

Problem

• Measuring technological convergence's multidimensional evolving nature accurately
• Assessing knowledge integration depth across technological boundaries precisely
• Quantifying diversity breadth of technological fields in convergence

Innovation

• Heterogeneous Graph Transformers analyze patent metadata
• Sentence-BERT models semantic integration across technologies
• Entropy Weight Method combines depth and breadth dimensions