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

Generative Representation Learning on Hyper-relational Knowledge Graphs via Masked Discrete Diffusion arXiv:2605.24064v1 Announce Type: cross Abstract: Hyper-relational knowledge graphs (HKGs) effectively represent complex facts. While inferring new knowledge in HKGs is a critical problem, current methods cast it as a simple link prediction, assuming that nearly all entities and relations within a fact are known, leaving only a single blank to be filled. However, this restricted assumption may