PSNEA: Pseudo-Siamese Network for Entity Alignment between Multi-modal Knowledge Graphs
Wenxin Ni, Qianqian Xu, Yangbangyan Jiang, Zongsheng Cao, Xiaochun Cao, Qingming Huang
Abstract
Multi-modal entity alignment aims to identify entities that refer to the same concept in the real world across a plethora of multi-modal knowledge graphs (MMKGs). Most existing methods focus on reducing the embedding differences between multiple modalities while neglecting the following challenges: 1) cannot handle the heterogeneity across graphs, 2) suffer from the scarcity of pre-aligned data (a.k.a. initial seeds). To tackle these issues, we propose a Pseudo-Siamese Network for multi-modal Entity Alignment (PSNEA). It consists of two modules to extract various information and generate holistic embeddings. Specifically, the first module PSN is designed with two parallel branches to learn the representations for different MMKGs, thus effectively bridging the graph heterogeneity. On top of this, we introduce an Incremental Alignment Pool (IAP) to alleviate the scarcity of initial seeds by labeling likely alignment. IAP avoids error-prone by data swapping and sample re-weighting strategies. To the best of our knowledge, PSNEA is the first model that tackles graph heterogeneity and scarcity of initial seeds in one unified framework. The extensive experiments demonstrate that our model achieves the best performance on both cross-lingual and cross-graph datasets. The source code is available at https://github.com/idrfer/psn4ea.