Hyperbolic embeddings haverecently gained attention in machine learning due totheir ability torepresent hierarchical data more accurately and succinctly than their Euclidean analogues.

In this paper, we propose MuRP, a theoretically inspired method to embed hierarchical multi-relational data in the Poincaré ball model of hyperbolic space. By considering the surface area of a hypersphere of increasing radius centered at a particular point, Euclidean space can be seen to "grow" polynomially,

FB15k's lack of hierarchy offers no advantage to hyperbolic embeddings, but its large number MuRP does not also set out to include MTL, but we hope to address this in future work. We will include all recommendations, e.g. However, we agree that it is important to compare models across a range of dimensionalities. Note that for MuRP with biases replaced by (transformed) norms, performance reduces (e.g. Multi-relational transforms and Justification for architecture: See "Architecture ablation study".