Large-scale Entity Alignment via Knowledge Graph Merging, Partitioning and Embedding
arXiv:2208.11125 · doi:10.1145/3511808.3557374
Abstract
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.
arXiv commentAccepted by CIKM 2022
References in corpus (8)
- A Survey on Knowledge Graphs: Representation, Acquisition and Applications
- Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
- A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs
- Eigenvector Centrality Distribution for Characterization of Protein Allosteric Pathways
- Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs
- Relational Reflection Entity Alignment
- Boosting the Speed of Entity Alignment 10*: Dual Attention Matching Network with Normalized Hard Sample Mining
- Informed Multi-context Entity Alignment