HMEA: A hierarchical medical knowledge graph entity alignment model fusing multi-aspect information

Medical entity alignment is crucial for the integration and reasoning of medical knowledge, aiming to match semantically equivalent entities across different medical knowledge graphs. Unlike entities in general knowledge graphs, medical entities contain rich multi-aspect information, which not only includes structural and attribute information but also additional information such as ontology and descriptions. However, existing entity alignment methods overlook these additional pieces of information and lack exploration into the fusion of multi-aspect information. This leads to less-than-ideal performance in medical entity alignment. To address the aforementioned issues, in this paper, we propose a hierarchical medical knowledge graph entity alignment method, termed HMEA, which integrates multi-aspect information. Firstly, we represent the medical knowledge graph as a hierarchical heterogeneous graph to model the multi-aspect information of medical entities. Secondly, we design different representation learning methods according to the characteristics of multi-aspect information to obtain vector representations of entities in different dimensions. Subsequently, we devise a two-stage multi-aspect knowledge fusion mechanism to dynamically integrate multi-aspect information, enabling mutual complementarity. Finally, we utilize the fused entity vector representations to guide entity alignment. We compare our approach with state-of-the-art baseline models on ten different types of publicly available datasets and further conduct ablation and parameter analyses. Experimental results validate the effectiveness and robustness of the proposed model. In benchmark tests across all datasets, HMEA outperforms the current state-of-the-art methods significantly.