An Accurate Unsupervised Method for Joint Entity Alignment and Dangling Entity Detection

Shengxuan Luo, Sheng Yu
2022 Findings of the Association for Computational Linguistics: ACL 2022   unpublished
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 laborconsuming. 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
more » ... ormation 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 crosslingual 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-ofthe-art EA methods by combining supervised EA data. For the DED task, UED obtains highquality results without supervision.
doi:10.18653/v1/2022.findings-acl.183 fatcat:of4p24ymcjcqncdshldnlmolbi