Entity alignment is critical for multiple knowledge graphs (KGs) integration. Although researchers have made significant efforts to explore the relational embeddings between different KGs, existing approaches may not describe multi-modal knowledge well in some tasks, e.g., entity alignment. In this paper, we propose DFMKE, a dual fusion multi-modal knowledge graph embedding framework, to address entity alignment. We first devise an early fusion method for fusing features of multi-modal entity representations of a KG. Simultaneously, multiple representations of various types of knowledge are generated independently by various techniques and fused by a low-rank multi-modal late fusion method. Finally, the outputs of early and late fusion methods are combined using a dual fusion scheme. DFMKE provides an ultimate fusion solution by leveraging the advantages of early and late fusion methods. Extensive experiments on two public datasets show that the DFMKE outperforms state-of-the-art methods by a significant margin achieving at least 10% more regard to Hits@n and MRR metrics.

实体对齐对于多知识图 (KG) 集成至关重要。尽管研究人员在探索不同 KG 之间的关系嵌入方面做出了巨大努力，但现有方法在某些任务（例如实体对齐）中可能无法很好地描述多模态知识。在本文中，我们提出了 DFMKE，一种对偶融合多模态知识图谱嵌入框架，以解决实体对齐问题。我们首先设计了一种早期融合方法，用于融合 KG 的多模态实体表示的特征。同时，通过各种技术独立生成各类知识的多种表示，并通过低秩多模态后期融合方法进行融合。最后，使用双重融合方案组合早期和晚期融合方法的输出。DFMKE 利用早期和晚期融合方法的优势，提供终极融合解决方案。在两个公共数据集上进行的大量实验表明，DFMKE 在 Hits@n 和 MRR 指标方面的表现比最先进的方法显着提高了至少 10%。