Adaptive Graph Convolutional Network for Knowledge Graph Entity Alignment

Renbo Zhu, Xukun Luo, Meng Ma, Ping Wang

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

Abstract

Entity alignment (EA) aims to identify equivalent entities from different Knowledge Graphs (KGs), which is a fundamental task for integrating KGs. Throughout its development, Graph Convolutional Network (GCN) has become one of the mainstream methods for EA. The key idea that GCN works in EA is that entities with similar neighbor structures are highly likely to be aligned. However, the noisy neighbors of entities transfer invalid information, drown out equivalent information, lead to inaccurate entity embeddings, and finally reduce the performance of EA. In this paper, we propose a lightweight framework with no training parameters for both supervised and unsupervised EA. Based on the Sinkhorn algorithm, we design a reliability measure for pseudo equivalent entities and propose Adaptive Graph Convolutional Network to deal with neighbor noises in GCN. During the training, the network dynamically updates the adaptive weights of relation triples to weaken the propagation of noises. Extensive experiments on benchmark datasets demonstrate that our framework outperforms the state-of-the-art methods in both supervised and unsupervised settings.

Original languageEnglish
Pages6040-6050
Number of pages11
StatePublished - 2022
Externally publishedYes
Event2022 Findings of the Association for Computational Linguistics: EMNLP 2022 - Abu Dhabi, United Arab Emirates
Duration: 7 Dec 202211 Dec 2022

Conference

Conference2022 Findings of the Association for Computational Linguistics: EMNLP 2022
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Period7/12/2211/12/22

Fingerprint

Dive into the research topics of 'Adaptive Graph Convolutional Network for Knowledge Graph Entity Alignment'. Together they form a unique fingerprint.

Cite this