Breaking barriers in hotspot mining: a novel approach to reflecting domain characteristics and correlations

Abstract

Hotspot mining is essential for acquiring information on hotspots and knowledge in a given domain, and it is also of great value for improving the efficiency and quality of scientific research work in the profession. Previous literature on hotspot mining did not take into account the domain characteristics of the literature and the diverse associations of the domain-specific literature itself. It is a challenging task to reflect the domain characteristics of the literature and use multiple correlations among the literature in the model. In this study, we depict each association link using a heterogeneous network of metallurgical literature and simultaneously fuse metallurgical domain-specific knowledge by aggregating the knowledge graph data of the neighbors into the term nodes of the heterogeneous network of the literature. A proposed heterogeneous academic network metallurgical literature hotspot mining method incorporates domain-specific knowledge. This method reflects various types of associational relation information in the literature via the heterogeneous network. In the meantime, it weights and analyzes the paths in the heterogeneous network, identifies the most critical paths for vectorized representation, and highlights the impact of essential paths and domain knowledge on representation learning, enhancing the information representation of diverse data in the model and improving its accuracy. The suggested model is compared with GCN, the MAGNN standard model, and its ablation model as applied to public and metallurgical literature datasets. The findings on the public dataset show that the proposed method is superior to the other two approaches. In contrast, the results for the metallurgical literature dataset are more conspicuous, with the proposed method exhibiting a more remarkable improvement in HR and NGCC.