Cross-Modal Graph Attention Network for Entity Alignment

The increasing popularity of multi-modal knowledge graphs (MMKGs) has led to a need for efficient entity alignment techniques that can exploit multi-modal information to integrate knowledge from different sources. GNN-based multi-modal entity alignment (MMEA) methods have achieved significant progress in entity alignment(EA) areas. However, these methods only rely on Graph Neural Networks (GNNs) to encode structural information, while ignoring visual and semantic modalities, which may lead to incomplete representation, thus how to integrate the visual and semantic information into GNN-based EA methods remains unexplored. In light of our insight that incorporating the message-passing mechanism of Graph Neural Networks to integrate multi-modal information is essential for fully exploiting the graph representation capability of GNN, we propose a novel Cross-modal Graph attention network for Entity Alignment (XGEA) that enables visual knowledge to interact with other views of the entity, including structural and literal information. We leverage the information from one modality as complementary relation information to compute the attention of another modality in the graph attention layers, enabling the learning of entity embedding by integrating multiple modalities. Moreover, the quantity of labeled data plays a crucial role in model performance, yet obtaining sufficient training data is expensive. To mitigate this issue, we use visual and semantic information to generate pseudo-pairs and propose a soft pseudo-labeling method for entity alignment to assign weights to the augmented training data to balance its quantity and quality. Extensive experiments show that our XGEA achieves superior performance consistently over the state-of-the-art MMEA baselines.