MERGE: A Modal Equilibrium Relational Graph Framework for Multi-Modal Knowledge Graph Completion - Yuying Shang; Kun Fu; Zequn Zhang; Li Jin; Zinan Liu; Shensi Wang; Shuchao Li (Sensors)
The multi-modal knowledge graph completion (MMKGC) task aims to automatically mine the missing factual knowledge from the existing multi-modal knowledge graphs (MMKGs), which is crucial in advancing cross-modal learning and reasoning. However, few methods consider the adverse effects caused by different missing modal information in the model learning process. To address the above challenges, we innovatively propose a Modal Equilibrium Relational Graph framEwork, called MERGE. By constructing three modal-specific directed relational graph attention networks, MERGE can implicitly represent missing modal information for entities by aggregating the modal embeddings from neighboring nodes. Subsequently, a fusion approach based on low-rank tensor decomposition is adopted to align multiple modal features in both the explicit structural level and the implicit semantic level, utilizing the structural information inherent in the original knowledge graphs, which enhances the interpretability of the fused features. Furthermore, we introduce a novel interpolation re-ranking strategy to adjust the importance of modalities during inference while preserving the semantic integrity of each modality. The proposed framework has been validated on four publicly available datasets, and the experimental results have demonstrated the effectiveness and robustness of our method in the MMKGC task.