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Learning Hierarchical Representations of Relational Data
Maximilian Nickel

Fri Dec 08 11:30 AM -- 12:00 PM (PST) @
in 6th Workshop on Automated Knowledge Base Construction (AKBC) »

Representation learning has become an invaluable approach for making statistical inferences from relational data. However, while complex relational datasets often exhibit a latent hierarchical structure, state-of-the-art embedding methods typically do not account for this property. In this talk, I will introduce a novel approach to learning such hierarchical representations of symbolic data by embedding them into hyperbolic space -- or more precisely into an n-dimensional Poincaré ball. I will discuss how the underlying hyperbolic geometry allows us to learn parsimonious representations which simultaneously capture hierarchy and similarity. Furthermore, I will show that Poincaré embeddings can outperform Euclidean embeddings significantly on data with latent hierarchies, both in terms of representation capacity and in terms of generalization ability.

Author Information

Maximilian Nickel (Facebook AI Research)

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