论文标题
通过知识图合并，分区和嵌入的大规模实体对齐
Large-scale Entity Alignment via Knowledge Graph Merging, Partitioning and Embedding
论文作者
论文摘要
实体对齐是知识图融合中的至关重要任务。但是，大多数实体对齐方法都有可伸缩性问题。最近的方法通过将大型公斤分成小块，以嵌入和对齐学习。但是，这样的分区和学习过程导致结构和一致性损失过多。因此，在这项工作中，我们提出了一种可扩展的基于GNN的实体对准方法，以从三个角度来减少结构和一致性损失。首先，我们提出一种基于中心的子图生成算法，以回顾一些具有不同子图之间桥梁的地标实体。其次，我们介绍了自我监督的实体重建，以从不完整的邻里子图中恢复实体表示形式，并设计了跨纸件负面抽样，以在对齐学习中纳入其他子图表中的实体。第三，在推论过程中，我们合并了子图的嵌入，以制作一个单个空间进行对齐搜索。基准开放数据集和提议的大型DBPEDIA1M数据集的实验结果验证了我们方法的有效性。
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.
