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Entity alignment (EA) aims to discover the equivalent entities in differentknowledge graphs (KGs), which play an important role in knowledge engineering.Recently, EA with dangling entities has been proposed as a more realisticsetting, which assumes that not all entities have corresponding equivalententities. In this paper, we focus on this setting. Some work has explored thisproblem by leveraging translation API, pre-trained word embeddings, and otheroff-the-shelf tools. However, these approaches over-rely on the sideinformation (e.g., entity names), and fail to work when the side information isabsent. On the contrary, they still insufficiently exploit the most fundamentalgraph structure information in KG. To improve the exploitation of thestructural information, we propose a novel entity alignment framework calledWeakly-Optimal Graph Contrastive Learning (WOGCL), which is refined on threedimensions : (i) Model. We propose a novel Gated Graph Attention Network tocapture local and global graph structure similarity. (ii) Training. Twolearning objectives: contrastive learning and optimal transport learning aredesigned to obtain distinguishable entity representations via the optimaltransport plan. (iii) Inference. In the inference phase, a PageRank-basedmethod is proposed to calculate higher-order structural similarity. Extensiveexperiments on two dangling benchmarks demonstrate that our WOGCL outperformsthe current state-of-the-art methods with pure structural information in bothtraditional (relaxed) and dangling (consolidated) settings. The code will bepublic soon.

Investigating Graph Structure Information for Entity Alignment with Dangling Cases