Abstract
Entity alignment (EA) which links equivalent entities across different knowledge graphs (KGs) plays a crucial role in knowledge fusion. In recent years, graph neural networks (GNNs) have been successfully applied in many embedding-based EA methods. However, existing GNN-based methods either suffer from the structural heterogeneity issue that especially appears in the real KG distributions or ignore the heterogeneous representation learning for unseen (unlabeled) entities, which would lead the model to overfit on few alignment seeds (i.e., training data) and thus cause unsatisfactory alignment performance. To enhance the EA ability, we propose GAEA, a novel EA approach based on graph augmentation. In this model, we design a simple Entity-Relation (ER) Encoder to generate latent representations for entities via jointly modeling comprehensive structural information and rich relation semantics. Moreover, we use graph augmentation to create two graph views for margin-based alignment learning and contrastive entity representation learning, thus mitigating the negative influence caused by structural heterogeneity and sparse seeds. Extensive experiments conducted on benchmark datasets demonstrate the effectiveness of our method. Our codes are available at https://github.com/Xiefeng69/GAEA.
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Acknowledgments
We thank reviewers for their helpful feedback. This work is supported by the National Natural Science Foundation of China No. 62172428.
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Xie, F., Zeng, X., Zhou, B., Tan, Y. (2023). Improving Knowledge Graph Entity Alignment with Graph Augmentation. In: Kashima, H., Ide, T., Peng, WC. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2023. Lecture Notes in Computer Science(), vol 13936. Springer, Cham. https://doi.org/10.1007/978-3-031-33377-4_1
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