  Knowledge graph integration typically suffers from the widely existing
dangling entities that cannot find alignment cross knowledge graphs (KGs). The
dangling entity set is unavailable in most real-world scenarios, and manually
mining the entity pairs that consist of entities with the same meaning is
labor-consuming. In this paper, we propose a novel accurate Unsupervised method
for joint Entity alignment (EA) and Dangling entity detection (DED), called
UED. The UED mines the literal semantic information to generate pseudo entity
pairs and globally guided alignment information for EA and then utilizes the EA
results to assist the DED. We construct a medical cross-lingual knowledge graph
dataset, MedED, providing data for both the EA and DED tasks. Extensive
experiments demonstrate that in the EA task, UED achieves EA results comparable
to those of state-of-the-art supervised EA baselines and outperforms the
current state-of-the-art EA methods by combining supervised EA data. For the
DED task, UED obtains high-quality results without supervision.
