The task of entity alignment between knowledge graphs (KGs) aims to identify
every pair of entities from two different KGs that represent the same entity.
Many machine learning-based methods have been proposed for this task. However,
to our best knowledge, existing methods all require manually crafted seed
alignments, which are expensive to obtain. In this paper, we propose the first
fully automatic alignment method named AutoAlign, which does not require any
manually crafted seed alignments. Specifically, for predicate embeddings,
AutoAlign constructs a predicate-proximity-graph with the help of large
language models to automatically capture the similarity between predicates
across two KGs. For entity embeddings, AutoAlign first computes the entity
embeddings of each KG independently using TransE, and then shifts the two KGs'
entity embeddings into the same vector space by computing the similarity
between entities based on their attributes. Thus, both predicate alignment and
entity alignment can be done without manually crafted seed alignments.
AutoAlign is not only fully automatic, but also highly effective. Experiments
using real-world KGs show that AutoAlign improves the performance of entity
alignment significantly compared to state-of-the-art methods.

这篇文章介绍了一种名为 AutoAlign 的全自动实体对齐方法，它利用大型语言模型构建谓词接近性图，通过计算实体属性的相似性将两个知识图谱的实体嵌入对齐到同一个向量空间，从而在没有手工标注种子对齐的情况下实现实体对齐，并且实验证明 AutoAlign 在实体对齐上相比于现有方法具有显著的性能提升。

AutoAlign: 全自动有效的知识图谱对齐

AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment  enabled by Large Language Models

Entity alignment typically suffers from the issues of structural
heterogeneity and limited seed alignments. In this paper, we propose a novel
Multi-channel Graph Neural Network model (MuGNN) to learn alignment-oriented
knowledge graph (KG) embeddings by robustly encoding two KGs via multiple
channels. Each channel encodes KGs via different relation weighting schemes
with respect to self-attention towards KG completion and cross-KG attention for
pruning exclusive entities respectively, which are further combined via pooling
techniques. Moreover, we also infer and transfer rule knowledge for completing
two KGs consistently. MuGNN is expected to reconcile the structural differences
of two KGs, and thus make better use of seed alignments. Extensive experiments
on five publicly available datasets demonstrate our superior performance (5%
Hits@1 up on average).

本文提出了一种新颖的多通道图神经网络模型（MuGNN），通过多通道鲁棒地编码两个知识图谱，以学习面向对齐的知识图谱嵌入。每个通道通过不同的关系加权方案对知识图谱进行编码，并通过汇聚技术进行进一步的合并。我们还推断并转移规则知识以便两个知识图谱一致完成。大量实验证明我们的方法可以更好地使用种子对齐，提高了 5％以上的平均 Hits@1 性能。