ConDNS: A Novel Conditional Diffusion-Based Negative Sampling Method for Knowledge Graph Embedding.

Knowledge Graph Embedding (KGE) maps entities and relations into continuous vector spaces to facilitate link prediction tasks. Given the inherent inability of knowledge graphs to directly supply high-quality negative samples with multi-level difficulty, existing methods typically rely on post-sampling assessment strategies, which lack controllable generation of difficulty-calibrated negatives tailored to diverse KGE training requirements. To address these challenges, we propose ConDNS, a novel conditional diffusion-based negative sampling method for knowledge graph embedding. By adjusting the diffusion timestep, our model achieves dynamic difficulty modulation of synthetic negatives through global entity-relation information utilization. This enables generation of semantically valid samples that synergistically integrate with conventional samples, thereby overcoming single-strategy sampling bottlenecks and establishing a multiscale difficulty configuration. Experiments demonstrate that ConDNS achieves state-of-the-art performance across multiple benchmarks with minimal synthetic samples while functioning as a plug-and-play module compatible with mainstream KGE architectures. Source code is available at: https://github.com/zrj-wang/ConDNS.