Multi-relational Poincar\'e Graph Embeddings

Understand

Hyperbolic embeddings have recently gained attention in machine learning due to their ability to represent hierarchical data more accurately and succinctly than their Euclidean analogues.

• However, multi-relational knowledge graphs often exhibit multiple simultaneous hierarchies, which current hyperbolic models do not capture.
• To address this, we propose a model that embeds multi-relational graph data in the Poincar\'e ball model of hyperbolic space.
• Our Multi-Relational Poincar\'e model (MuRP) learns relation-specific parameters to transform entity embeddings by M\"obius matrix-vector multiplication and M\"obius addition.