Multimodal Knowledge Graph Completion via Relation-Aware Negative Sampling with Diffusion-based Interpolation

Multimodal Knowledge Graphs (MMKGs) enable structured reasoning across heterogeneous modalities and are essential infrastructure for data management and analytics. As MMKGs are inherently incomplete and generally contain noisy data, MMKG Completion (MMKGC) is a central task for improving data quality and semantic inference. Specifically, a crucial aspect of MMKGC is negative sampling , which impacts model discriminability and completion accuracy. However, existing negative sampling proposals often ignore the semantic properties of relation types and lack mechanisms for adaptive control of negative sample hardness, leading to suboptimal MMKGC performance. To address issues such as these, we propose RelDINS that improves semantic consistency and robustness by performing relation-type-aware negative sampling through diffusion-based interpolation. RelDINS incorporates two modules: (i) a Relation-type-aware Multimodal Embedding Learning (RMEL) module that adaptively injects relational semantics into entity representations based on cardinality constraints; (ii) and a Diffusion-based Interpolation Negative Sampling (DINS) module that dynamically generates hardness-tunable negative samples via spherical linear interpolation in diffusion noise space. Extensive experiments on three public benchmarks show that RelDINS achieves state-of-the-art performance, with average improvements of 3.5% in MRR, 5.0% in Hit@1, 2.6% in Hit@3, and 1.4% in Hit@10 over leading baselines. Supported by a complexity analysis and an empirical study, RelDINS is a principled and scalable solution to enhancing semantic consistency and data reliability in MMKGC.