Learning Hierarchical Representations of Relational Data

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.