Towards Knowledge Graphs Federations: Issues and Technologies

Zhao, Xiang

doi:10.1007/978-981-16-0479-9_6

Cited by 1 publication

(1 citation statement)

References 33 publications

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“…Moreover, the embedding-level EA methods evaluate their performances only on the existing entity pairs between the testing KGs. However, in various real-world scenarios, the identical pair of one entity is not guaranteed to exist between different KGs (Zhao, 2020;Sun et al, 2021), which limits the application range of embedding-based EA methods.…”

Section: Introductionmentioning

confidence: 99%

Semi-constraint Optimal Transport for Entity Alignment with Dangling Cases

Luo¹,

Cheng²,

Yu³

2022

Preprint

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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 semiconstraint 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.

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Section: Introductionmentioning

confidence: 99%