Unifying Within and Across: Intra-Modality Multi-View Fusion and Inter-Modality Alignment for Knowledge Graph Completion

Abstract

Multi-modal knowledge graph completion (MMKGC) enhances the structural and semantic richness of knowledge graphs by integrating diverse information across modalities. However, existing methods often either overlook the diversity within a single modality or fail to ensure effective cross-modality alignment for entity representation. This leads to suboptimal entity representations, as inconsistent or irrelevant data is treated uniformly. To address these challenges, we propose a unified framework that combines intra-modality multi-view fusion with cross-modality alignment (IMVIA for short). Our approach captures the most relevant information within each modality by leveraging relational context. Simultaneously, we apply information disentanglement and contrastive learning, allowing each modality-specific learner to focus on extracting distinctive features while maintaining consistent training objectives across all modalities. Furthermore, we employ a relation-aware gated decision fusion network to robustly integrate diverse information. Experimental results show that IMVIA significantly outperforms state-of-the-art approaches across multiple benchmark datasets, validating its effectiveness and robustness in MMKGC task.