UniKG: A Benchmark and Universal Embedding for Large-Scale Knowledge Graphs

1 code implementation • 11 Sep 2023 • Yide Qiu, Shaoxiang Ling, Tong Zhang, Bo Huang, Zhen Cui

To perform effective learning on the large-scale UniKG, two key measures are taken, including (i) the semantic alignment strategy for multi-attribute entities, which projects the feature description of multi-attribute nodes into a common embedding space to facilitate node aggregation in a large receptive field; (ii) proposing a novel plug-and-play anisotropy propagation module (APM) to learn effective multi-hop anisotropy propagation kernels, which extends methods of large-scale homogeneous graphs to heterogeneous graphs.

Attribute Graph Learning +3