Entity Alignment (EA) has attracted widespread attention in both academia and
industry, which aims to seek entities with same meanings from different
Knowledge Graphs (KGs). There are substantial multi-step relation paths between
entities in KGs, indicating the semantic relations of entities. However,
existing methods rarely consider path information because not all natural paths
facilitate for EA judgment. In this paper, we propose a more effective entity
alignment framework, RPR-RHGT, which integrates relation and path structure
information, as well as the heterogeneous information in KGs. Impressively, an
initial reliable path reasoning algorithm is developed to generate the paths
favorable for EA task from the relation structures of KGs, which is the first
algorithm in the literature to successfully use unrestricted path information.
In addition, to efficiently capture heterogeneous features in entity
neighborhoods, a relation-aware heterogeneous graph transformer is designed to
model the relation and path structures of KGs. Extensive experiments on three
well-known datasets show RPR-RHGT significantly outperforms 11 state-of-the-art
methods, exceeding the best performing baseline up to 8.62% on Hits@1. We also
show its better performance than the baselines on different ratios of training
set, and harder datasets.

提出了一种名为 RPR-RHGT 的实体对齐框架，它整合了知识图中的关系和路径结构信息以及异构信息，并成功地使用无限制路径信息来生成有益于实体对齐任务的路径，同时设计了一种关系感知的异构图变换器来模拟知识图的关系和路径结构，实验结果表明，RPR-RHGT 在三个知名数据集上的表现明显优于 11 种现有方法，在 Hits@1 上最大可达 8.62％。

可靠路径推理和关系感知异构图变换的实体对齐

Entity Alignment with Reliable Path Reasoning and Relation-Aware Heterogeneous Graph Transformer

Recent years have witnessed the emerging success of graph neural networks
(GNNs) for modeling structured data. However, most GNNs are designed for
homogeneous graphs, in which all nodes and edges belong to the same types,
making them infeasible to represent heterogeneous structures. In this paper, we
present the Heterogeneous Graph Transformer (HGT) architecture for modeling
Web-scale heterogeneous graphs. To model heterogeneity, we design node- and
edge-type dependent parameters to characterize the heterogeneous attention over
each edge, empowering HGT to maintain dedicated representations for different
types of nodes and edges. To handle dynamic heterogeneous graphs, we introduce
the relative temporal encoding technique into HGT, which is able to capture the
dynamic structural dependency with arbitrary durations. To handle Web-scale
graph data, we design the heterogeneous mini-batch graph sampling
algorithm---HGSampling---for efficient and scalable training. Extensive
experiments on the Open Academic Graph of 179 million nodes and 2 billion edges
show that the proposed HGT model consistently outperforms all the
state-of-the-art GNN baselines by 9%--21% on various downstream tasks.

本文介绍了一种用于建模 Web 规模异构图的异构图转换器架构（HGT），通过设计基于节点类型和边类型的参数来实现异构性，并引入相对时间编码技术和异构迷你批图采样算法来处理动态异构图数据，实验表明，所提出的 HGT 模型在各种下游任务中始终优于所有最先进的 HNN 基线 9％至 21％。