MMFusion: Query-Guided Cross-Fusion for Robust Multimodal Knowledge Graph Completion

Multimodal knowledge graph completion enriches link prediction by combining structured triples with auxiliary modalities such as text and images, but existing solutions often rely on relation-insensitive fusion and are brittle under instance-level modality noise, leading to weak alignment between entity evidence and relational semantics. We propose MMFusion, a unified reasoning framework with three key designs. First, Relation-Guided Entity Transformation (RGET) constructs relation-conditioned entity views within each modality to enhance entity–relation compatibility. Second, Adaptive Modality Gating (AMG) performs instance-level gating across structural, visual, and textual signals to suppress noisy modalities and improve fusion robustness. Finally, Query-Guided Cross-modal Fusion (QCF) builds a unified relation-conditioned query via dual-stream attention between entity and relation modalities, enabling deep cross-modal alignment for link prediction. Extensive experiments on four public multimodal knowledge graph datasets show that MMFusion consistently outperforms strong baselines, especially in sparse and noisy settings.