Aligning entity of knowledge graph based on graph convolutional network with maximum translated similarity
- 1School of Computer and Network Engineering, Shanxi Datong University, Datong, China
Abstract
Entity alignment is a pivotal technology for integrating heterogeneous knowledge graphs, yet most embedding-based alignment methods destroy the inherent structural embedding information of knowledge graphs when unifying cross-graph embedding spaces, resulting in limited alignment accuracy and stability. To tackle this issue, this paper proposes a novel model for entity alignment tasks, which named as Graph Convolutional Network with Maximum Translated Similarity (GCN-MTS). GCN-MTS innovatively leverages a maximum translated similarity mechanism to optimize the adjacency matrix, degree matrix and loss function. This core design effectively maintains the original relative embedding structure of cross-graph entities during embedding space unification, which is the key improvement over existing approaches. Comprehensive experiments on 3 pairs of real-world knowledge graph dataset demonstrate the superior performance of GCN-MTS against advanced baseline methods. It achieves average performance gains of 5%–15% in Hits@1 and 4%–16% in Hits@10. Ablation results confirm that the optimized module brings a nearly 22% improvement in alignment metrics compared with the vanilla GCN. Furthermore, GCN-MTS presents excellent robustness and stability across different seed alignment quantities and seed combinations.
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BibTeX
@article{zhou2026aligning,
title = {{Aligning entity of knowledge graph based on graph convolutional network with maximum translated similarity}},
author = {Jianhui Zhou and Yun Gao and Xiangbing Zhao and Yanping Guo and Xulong Zhang},
journal = {PLOS One},
year = {2026},
month = nov,
doi = {10.1371/journal.pone.0362390},
url = {https://doi.org/10.1371/journal.pone.0362390},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Aligning entity of knowledge graph based on graph convolutional network with maximum translated similarity
AU - Jianhui Zhou
AU - Yun Gao
AU - Xiangbing Zhao
AU - Yanping Guo
AU - Xulong Zhang
PY - 2026
DA - 2026/11/06
JO - PLOS One
DO - 10.1371/journal.pone.0362390
UR - https://doi.org/10.1371/journal.pone.0362390
AB - Entity alignment is a pivotal technology for integrating heterogeneous knowledge graphs, yet most embedding-based alignment methods destroy the inherent structural embedding information of knowledge graphs when unifying cross-graph embedding spaces, resulting in limited alignment accuracy and stability. To tackle this issue, this paper proposes a novel model for entity alignment tasks, which named as Graph Convolutional Network with Maximum Translated Similarity (GCN-MTS). GCN-MTS innovatively leverages a maximum translated similarity mechanism to optimize the adjacency matrix, degree matrix and loss function. This core design effectively maintains the original relative embedding structure of cross-graph entities during embedding space unification, which is the key improvement over existing approaches. Comprehensive experiments on 3 pairs of real-world knowledge graph dataset demonstrate the superior performance of GCN-MTS against advanced baseline methods. It achieves average performance gains of 5%–15% in Hits@1 and 4%–16% in Hits@10. Ablation results confirm that the optimized module brings a nearly 22% improvement in alignment metrics compared with the vanilla GCN. Furthermore, GCN-MTS presents excellent robustness and stability across different seed alignment quantities and seed combinations.
SN - 1932-6203
ER -