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[R] Multi-relational Poincaré Graph Embeddings

[R] Multi-relational Poincaré Graph Embeddings

https://i.redd.it/eonsgsm74q031.png

Paper: https://arxiv.org/abs/1905.09791

PyTorch Code: https://github.com/ibalazevic/multirelational-poincare

Key contributions:

  • proposing MuRP, a theoretically inspired method to embed hierarchical multi-relational data in the Poincaré ball model of hyperbolic space which achieves state-of-the-art results on the hierarchical WN18RR knowledge graph dataset;
  • showing that our Poincaré embeddings require far fewer dimensions than Euclidean embeddings to achieve comparable performance; and
  • visualizing the learned embeddings and analyzing the properties of the Poincaré model compared to its Euclidean analogue.

Abstract:

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 multi-relational Poincaré embeddings outperform their Euclidean counterpart and existing embedding methods on the link prediction task, particularly at lower dimensionality.

submitted by /u/ibalazevic
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.