Improving Knowledge Graph Entity Alignment with Graph Augmentation
- URL: http://arxiv.org/abs/2304.14585v1
- Date: Fri, 28 Apr 2023 01:22:47 GMT
- Title: Improving Knowledge Graph Entity Alignment with Graph Augmentation
- Authors: Feng Xie, Xiang Zeng, Bin Zhou, Yusong Tan
- Abstract summary: 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.
We propose graph augmentation to create two graph views for margin-based alignment learning and contrastive entity representation learning.
- Score: 11.1094009195297
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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 structural heterogeneity and further improving the model's alignment
performance. Extensive experiments conducted on benchmark datasets demonstrate
the effectiveness of our method.
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