Low-Dimensional Hyperbolic Knowledge Graph Embeddings

Understand

Knowledge graph (KG) embeddings learn low-dimensional representations of entities and relations to predict missing facts.

• KGs often exhibit hierarchical and logical patterns which must be preserved in the embedding space.
• For hierarchical data, hyperbolic embedding methods have shown promise for high-fidelity and parsimonious representations.
• However, existing hyperbolic embedding methods do not account for the rich logical patterns in KGs.