Role-Aware Hierarchical Sub-Entity Alignment and Difficulty-Weighted Negative Sampling for Multi-Modal Knowledge Graph Completion

Multi-modal knowledge graph completion (MMKGC) aims to infer missing facts by jointly exploiting structural, visual, and textual evidence. However, existing methods often suffer from two limitations: (i) coarse entity-level fusion overlooks relation- and role-specific correspondences between fine-grained multimodal sub-entities, and (ii) hard-negative training may amplify false-negative bias by heavily penalizing plausible but unobserved triples. To address these issues, we propose RHDN, a unified framework for role-aware multimodal representation learning and false-negative-aware training. RHDN first introduces a role-aware hierarchical alignment module that constructs relation- and role-conditioned optimal transport couplings between visual and textual sub-entities and aggregates coupling-weighted pairwise interactions with hierarchical role prototypes, yielding fine-grained relation-aware entity representations. On the training side, RHDN explicitly decouples negative difficulty from false-negative risk: model scores characterize hardness, while multimodal role-consistency evidence provides an independent risk signal. The two factors are combined through KL-regularized reweighting to preserve informative hard negatives while reducing the influence of high-risk candidates. Experiments on three widely used MMKGC benchmarks demonstrate consistent improvements over strong unimodal, multimodal, and negative-sampling baselines, with further analyses validating the contributions of the proposed components.