Self-supervised Entity Alignment Model Based on Joint Embedding

Abstract

Abstract Entity alignment is an important problem for constructing Web-scale KGs. Self-supervised entity alignment models represented by SelfKG, proposed in 2022, have eliminated the essential requirement of accurate alignment: human labeling, which significantly saves resources. However, when SelfKG scales up to improve entity alignment accuracy, it introduces neighbor noise, leading to a decrease in alignment performance. This indicates that the ability of self-supervised models to aggregate and utilize knowledge graph information needs further exploration. To address this issue, we proposed the following approach: using a Graph Convolutional Network(GCN) to collect global entity and structural information, and a Graph Attention Network(GAT) to collect local neighborhood information from subgraphs. Subsequently, we introduced a novel fusion method to merge the two parts through weighted sum and residual connection, and completed self-supervised entity alignment using the Relative Similarity Metric(RSM) mechanism proposed by SelfKG. This model has both the ability to scale up and the flexibility to avoid neighbor noise, resulting in a significant improvement in performance.