An radicals construction technique based on dual quaternions and hierarchical transformers

Abstract

At present, the exploration of Knowledge Graph Embedding (KGE) mainly focuses on static KGE models. Due to the diversity and complexity of vertical domain data, it is difficult for existing KGE models to achieve excellent performance. Specific KGE models must be constructed for research on vertical domain knowledge graphs. This paper explores the relationships between the composition of Chinese characters and their sounds, meanings, and forms by constructing the Chinese Radical Knowledge Graph (CRKG). CRKG was constructed from a unique perspective according to the characteristics of Chinese radicals and knowledge graphs. The minor components of Chinese characters are separated and constructed into the fourth dimension of the knowledge graph. In this paper, we propose a new Chinese Radical Knowledge Graph Embedding (CRKGE) model, RotAL, which introduces dual quaternions and hierarchical transformers into KGE. RotAL performs a translation-rotation operation on the head entity with the relation as a parameter, then does a translation operation on the head entity with the minor component as a parameter to be close to the tail entity. Finally, we feed the trained quaternions into hierarchical transformers to jointly learn entity-related composition and relational contextualization based on a head entity’s neighborhood. We demonstrate that our method can model and infer various relational patterns of CRKG, such as symmetric/antisymmetric, reflexive, inversion, and multiple. The experimental results on four CRKG datasets demonstrate that the RotAL model achieves the highest Hit@1 score, outperforming existing KGE models in accurately identifying the target entity on the first attempt. Furthermore, the RotAL model consistently achieves superior performance across other evaluation metrics, substantiating its significant advantage over existing models.