Aligned-entities-Based Fusion Embedding on Hetero-field Knowledge Graphs
Peng Xiao, Chao Liu, Weijia Jia, Lijun Dong · Data Intelligence · 2025
The means of knowledge graph embedding is to transform entities and relations into low-dimensional vectors. When it is necessary to obtain the embedding results of two hetero-field knowledge graphs in a unified vector space, there are only a few aligned entities between them, previous methods first need to merge the two graphs into a large graph, and then re-embed the entire large graph. This ignores the potential reuse of the original representation embeddings of two knowledge graphs and will lead to a lot of time consumption. To address this problem, this paper proposes a hetero-field knowledge graph embedding fusion model (BlockEF) based on aligned entities. According to the fact that the aligned entities of the two graphs should be located in the same position in the vector space, the transformation relationship between the embeddings of two graphs is firstly obtained, and then graph embedding is fine-tuned and optimized to achieve efficient fusion of hetero-field knowledge graph embeddings. The experimental results show that our method can significantly reduce the computational burden of hetero-field knowledge graph embedding fusion and ensure the quality of embedding fusion.