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Entity alignment is a crucial task in knowledge graph fusion. However, mostentity alignment approaches have the scalability problem. Recent methodsaddress this issue by dividing large KGs into small blocks for embedding andalignment learning in each. However, such a partitioning and learning processresults in an excessive loss of structure and alignment. Therefore, in thiswork, we propose a scalable GNN-based entity alignment approach to reduce thestructure and alignment loss from three perspectives. First, we propose acentrality-based subgraph generation algorithm to recall some landmark entitiesserving as the bridges between different subgraphs. Second, we introduceself-supervised entity reconstruction to recover entity representations fromincomplete neighborhood subgraphs, and design cross-subgraph negative samplingto incorporate entities from other subgraphs in alignment learning. Third,during the inference process, we merge the embeddings of subgraphs to make asingle space for alignment search. Experimental results on the benchmark OpenEAdataset and the proposed large DBpedia1M dataset verify the effectiveness ofour approach.

Large-scale Entity Alignment via Knowledge Graph Merging, Partitioning and Embedding