Work

Technology convergence prediction based on temporal heterogeneous graph neural networks

Year: 2024

Type: article

Abstract: Most studies on technology convergence prediction typically focus on the co-occurrence relationship of technology elements in patent data, neglecting the joint effects of internal and external factors on technology convergence. This study constructs a novel temporal heterogeneous graph neural networks-based technology convergence prediction framework considering structural and semantic features (THGNN-TCP) to dynamically identify technology opportunities at multiple granularities. The framework takes into account the co-occurrence relationship, semantic distance and knowledge flow among technologies, as well as the linkage between science and technology to capture the changes in the internal and external influencing factors of technology. Specifically, we introduce a node type-aware strategy and a talking-heads attention mechanism to capture the structural features among technologies, while leveraging LSTM to discern the temporal dependencies inherent in these technological interactions. Our experiments on datasets in the field of large language models demonstrate that THGNN-TCP significantly outperforms a series of baseline approaches in terms of AUC and F1 metrics, and is capable of identifying potential technological convergence opportunities from a multi-granularity perspective.

Source: Information Processing & Management

DOI: 10.1016/j.ipm.2024.104034

Authors: Hui Li, Huanrong Liang, Yaohua Hu, Xiaojie Liu

Institutions: Xidian University, Chinese Academy of Sciences, National Science Library

Language: English

Topic: Intellectual Property and Patents

Subfield: Management of Technology and Innovation

Field: Business, Management and Accounting

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,350