Conflict-Aware Pseudo Labeling via Optimal Transport for Entity Alignment

Qijie Ding, Daokun Zhang, Jie Yin

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review


Entity alignment aims to discover unique equivalent entity pairs with the same meaning across different knowledge graphs (KGs). Existing models have focused on projecting KGs into a latent embedding space so that inherent semantics between entities can be captured for entity alignment. However, the adverse impacts of alignment conflicts have been largely overlooked during training, thereby limiting the entity alignment performance. To address this issue, we propose a novel Conflict-aware Pseudo Labeling via Optimal Transport model (CPL-OT) for entity alignment. The key idea is to iteratively pseudo-label alignment pairs empowered with conflict-aware optimal transport (OT) modeling to boost the precision of entity alignment. CPL-OT is composed of two key components - entity embedding learning with global-local aggregation and iterative conflict-aware pseudo labeling - that mutually reinforce each other. To mitigate alignment conflicts during pseudo labeling, we propose to use optimal transport as an effective means to warrant one-to-one entity alignment between two KGs with the minimal overall transport cost. Extensive experiments on benchmark datasets validate the superiority of CPL-OT over state-of-the-art baselines under both settings with and without prior alignment seeds.

Original languageEnglish
Title of host publicationProceedings - 22nd IEEE International Conference on Data Mining, ICDM 2022
EditorsXingquan Zhu, Sanjay Ranka, My T. Thai, Takashi Washio, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665450997
Publication statusPublished - 2022
Externally publishedYes
Event22nd IEEE International Conference on Data Mining, ICDM 2022 - Orlando, United States
Duration: 28 Nov 20221 Dec 2022

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Conference22nd IEEE International Conference on Data Mining, ICDM 2022
Country/TerritoryUnited States


  • entity alignment
  • knowledge graph
  • optimal transport
  • pseudo labeling

ASJC Scopus subject areas

  • General Engineering


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