  Entity alignment (EA) merges knowledge graphs (KGs) by identifying the
equivalent entities in different graphs, which can effectively enrich knowledge
representations of KGs. However, in practice, different KGs often include
dangling entities whose counterparts cannot be found in the other graph, which
limits the performance of EA methods. To improve EA with dangling entities, we
propose an unsupervised method called Semi-constraint Optimal Transport for
Entity Alignment in Dangling cases (SoTead). Our main idea is to model the
entity alignment between two KGs as an optimal transport problem from one KG's
entities to the others. First, we set pseudo entity pairs between KGs based on
pretrained word embeddings. Then, we conduct contrastive metric learning to
obtain the transport cost between each entity pair. Finally, we introduce a
virtual entity for each KG to "align" the dangling entities from the other KGs,
which relaxes the optimization constraints and leads to a semi-constraint
optimal transport. In the experimental part, we first show the superiority of
SoTead on a commonly-used entity alignment dataset. Besides, to analyze the
ability for dangling entity detection with other baselines, we construct a
medical cross-lingual knowledge graph dataset, MedED, where our SoTead also
reaches state-of-the-art performance.
