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.

本文提出了一种可扩展的基于 GNN 的实体对齐方法，通过三个视角减少子图嵌入中的结构和对齐损失，并在实验中证实了该方法的有效性。

知识图谱合并、分割和嵌入实现大规模实体对齐

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

To alleviate the challenges of building Knowledge Graphs (KG) from scratch, a
more general task is to enrich a KG using triples from an open corpus, where
the obtained triples contain noisy entities and relations. It is challenging to
enrich a KG with newly harvested triples while maintaining the quality of the
knowledge representation. This paper proposes a system to refine a KG using
information harvested from an additional corpus. To this end, we formulate our
task as two coupled sub-tasks, namely join event extraction (JEE) and knowledge
graph fusion (KGF). We then propose a Collaborative Knowledge Graph Fusion
Framework to allow our sub-tasks to mutually assist one another in an
alternating manner. More concretely, the explorer carries out the JEE
supervised by both the ground-truth annotation and an existing KG provided by
the supervisor. The supervisor then evaluates the triples extracted by the
explorer and enriches the KG with those that are highly ranked. To implement
this evaluation, we further propose a Translated Relation Alignment Scoring
Mechanism to align and translate the extracted triples to the prior KG.
Experiments verify that this collaboration can both improve the performance of
the JEE and the KGF.

本文提出了一种协作知识图谱融合框架，旨在使用从其他语料库中收集到的信息来精细化已有的知识图谱，并改进联合事件抽取与知识图谱融合的性能。