  Entity alignment (EA) aims to discover the equivalent entities in different
knowledge graphs (KGs), which play an important role in knowledge engineering.
Recently, EA with dangling entities has been proposed as a more realistic
setting, which assumes that not all entities have corresponding equivalent
entities. In this paper, we focus on this setting. Some work has explored this
problem by leveraging translation API, pre-trained word embeddings, and other
off-the-shelf tools. However, these approaches over-rely on the side
information (e.g., entity names), and fail to work when the side information is
absent. On the contrary, they still insufficiently exploit the most fundamental
graph structure information in KG. To improve the exploitation of the
structural information, we propose a novel entity alignment framework called
Weakly-Optimal Graph Contrastive Learning (WOGCL), which is refined on three
dimensions : (i) Model. We propose a novel Gated Graph Attention Network to
capture local and global graph structure similarity. (ii) Training. Two
learning objectives: contrastive learning and optimal transport learning are
designed to obtain distinguishable entity representations via the optimal
transport plan. (iii) Inference. In the inference phase, a PageRank-based
method is proposed to calculate higher-order structural similarity. Extensive
experiments on two dangling benchmarks demonstrate that our WOGCL outperforms
the current state-of-the-art methods with pure structural information in both
traditional (relaxed) and dangling (consolidated) settings. The code will be
public soon.
