AI-enhanced multi-dimensional measurement of technological convergence through heterogeneous graph and semantic learning
Siming Deng, Yi Zhang, Mengjia Wu, Chunjuan Luan, Runsong Jia
Scientometrics
Problems Identified (2)
Technological convergence measurement challenge: Accurately measuring technological convergence is difficult because convergence is multidimensional and evolves over time.
Technological convergence measurement challenge: Accurately measuring technological convergence is difficult because convergence is multidimensional and evolves over time.
Proposed Solutions (5)
AI-enhanced Technological Convergence Index: The study develops an AI-enhanced Technological Convergence Index that measures technological convergence through depth and breadth dimensions.
Heterogeneous graph and semantic depth modeling: The depth dimension uses IPC textual descriptions and patent metadata in a heterogeneous graph modeled with HGT and SBERT to represent knowledge integration across technological boundaries.
Shannon diversity breadth measurement: The breadth dimension quantifies the diversity of technological fields in patents using the Shannon Diversity Index.
Entropy-weighted index aggregation: The final TCI combines depth and breadth using the Entropy Weight Method to assign objective weights based on information entropy.
AI-enhanced Technological Convergence Index: The study develops an AI-enhanced Technological Convergence Index that measures technological convergence through depth and breadth dimensions.
Results (3)
Comparative advantages over established measures:
Empirical reliability via robustness test:
Positive effect on patent quality:
Research Domain
Technological convergence measurement; patent analytics; scientometrics