Generative Representation Learning on Hyper-relational Knowledge Graphs via Masked Discrete Diffusion

In three linesKREPE, a generative representation learning method for hyper-relational knowledge graphs, uses masked discrete diffusion to generate complete facts from partially observed queries. Unifies link prediction and fact generation in a single framework, outperforming LLM-based baselines on standard benchmarks.

Summary generated by Claude — human-verified