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Multi-relational Poincaré Graph Embeddings
Ivana Balazevic · Carl Allen · Timothy Hospedales

Wed Dec 11 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #68

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 Poincaré ball model of hyperbolic space. Our Multi-Relational Poincaré model (MuRP) learns relation-specific parameters to transform entity embeddings by Möbius matrix-vector multiplication and Möbius addition. Experiments on the hierarchical WN18RR knowledge graph show that our Poincaré embeddings outperform their Euclidean counterpart and existing embedding methods on the link prediction task, particularly at lower dimensionality.

Author Information

Ivana Balazevic (University of Edinburgh)
Carl Allen (University of Edinburgh)
Timothy Hospedales (University of Edinburgh)

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