Large-scale Entity Alignment via Knowledge Graph Merging, Partitioning and Embedding. (arXiv:2208.11125v1 [cs.LG])

Entity alignment is a crucial task in knowledge graph fusion. However, most
entity alignment approaches have the scalability problem. Recent methods
address this issue by dividing large KGs into small blocks for embedding and
alignment learning in each. However, such a partitioning and learning process
results in an excessive loss of structure and alignment. Therefore, in this
work, we propose a scalable GNN-based entity alignment approach to reduce the
structure and alignment loss from three perspectives. First, we propose a
centrality-based subgraph generation algorithm to recall some landmark entities
serving as the bridges between different subgraphs. Second, we introduce
self-supervised entity reconstruction to recover entity representations from
incomplete neighborhood subgraphs, and design cross-subgraph negative sampling
to incorporate entities from other subgraphs in alignment learning. Third,
during the inference process, we merge the embeddings of subgraphs to make a
single space for alignment search. Experimental results on the benchmark OpenEA
dataset and the proposed large DBpedia1M dataset verify the effectiveness of
our approach.