Papers › Poincaré Embeddings for Learning Hierarchical Representations
Poincaré Embeddings for Learning Hierarchical Representations
Maximilian Nickel, Douwe Kiela
Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs. However, while complex symbolic datasets often exhibit a latent hierarchical structure, state-of-the-art methods typically learn embeddings in Euclidean vector spaces, which do not account for this property. For this purpose, we introduce a new approach for learning hierarchical representations of symbolic data by embedding them into hyperbolic space -- or more precisely into an n-dimensional Poincar\'e ball. Due to the underlying hyperbolic geometry, this allows us to learn parsimonious representations of symbolic data by simultaneously capturing hierarchy and similarity. We introduce an efficient algorithm to learn the embeddings based on Riemannian optimization and show experimentally that Poincar\'e embeddings outperform Euclidean embeddings significantly on data with latent hierarchies, both in terms of representation capacity and in terms of generalization ability.
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Code
Syntology Ran 6 of 14 code samples harvested from 4 repositories linked to this paper; 8 have no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · violated contract; 2 ran · our draft was wrong; 2 ran · fixture could not drive it.
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Link Prediction | WordNet | Poincare Embeddings (dim=100) | Accuracy | 77.4 | #2 of 7 | Archive leaderboard | report |
| Link Prediction | WordNet | Poincare Embeddings (dim=50) | Accuracy | 77.0 | #3 of 7 | Archive leaderboard | report |
| Link Prediction | WordNet | Poincare Embeddings (dim=20) | Accuracy | 74.3 | #4 of 7 | Archive leaderboard | report |
| Link Prediction | WordNet | Poincare Embeddings (dim=10) | Accuracy | 68.3 | #5 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: Poincaré Embeddings
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