Uncovering emerging technologies in intelligent manufacturing via graph classification of community characteristics

Abstract

Advancing the identification of emerging technology trends is pivotal for informed design decisions and risk mitigation. Our research introduces a novel methodology, centered on technology subgraphs within a patent knowledge graph, utilising graph neural networks combined with data augmentation techniques. This approach deviates from traditional methods, which primarily focus on individual patent nodes or the entire patent network. Instead, it segments the patent knowledge graph into subgraphs, analyzing them based on structural features to identify emerging technologies. Our method is further strengthened by employing an expert-curated emerging technology tag library, enabling a quantitative, rather than solely qualitative, assessment of the subgraphs’ relevance to emerging technologies. Additionally, we integrate comparative learning to enhance subgraph data quality, thereby boosting the reliability and accuracy of our approach. The results demonstrate a significant improvement in the accurate identification of emerging technologies, underscoring the effectiveness of our proposed model.