Multi-modal Entity Alignment via Position-enhanced Multi-label Propagation

Abstract

Multi-modal Entity Alignment (MMEA) refers to utilizing multiple modalities such as text, images, videos, etc., to match entities from multiple knowledge graphs. Compared to single-modal entity alignment, multi-modal entity alignment can provide a more comprehensive description of entity semantics and improve matching accuracy. Currently, research efforts are directed towards the development of sophisticated deep learning models, such as graph neural networks, that can effectively capture and integrate the multi-modal features of entities for entity alignment tasks. While these models have shown promising results, they tend to focus on capturing only the local structure of entities, leading to the challenge of subgraph isomorphism. Moreover, the complexity of these models often hinders their scalability. To address these limitations, this paper proposes a non-neural, position-enhanced multi-modal entity alignment algorithm that leverages the label propagation technique to fuse and aggregate various multi-modal and position features, resulting in entity representations that are aware of long-term alignment information. Extensive experiments on various public datasets demonstrate that our proposed approach outperforms state-of-the-art algorithms in terms of both alignment accuracy and computational efficiency.