Yu Ni

AdaGeM: Adaptive Geometric Learning on Manifolds for Hyper-Relational Knowledge Graphs

Hyper-relational Knowledge Graphs (HKGs) extend traditional knowledge graphs by introducing high-order dependencies, where auxiliary qualifiers provide detailed information. However, modeling such intricate topologies (e.g., mixtures of hierarchies, cycles, and chains) is challenging. Existing metho