  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.
