Multi-relational Poincaré Graph Embeddings
This addresses the challenge of representing complex hierarchical structures in knowledge graphs for tasks like link prediction, though it is incremental as it builds on existing hyperbolic embedding methods.
The paper tackled the problem of embedding multi-relational knowledge graphs with multiple hierarchies by proposing a hyperbolic model, achieving improved link prediction performance, especially at lower dimensionality, on the WN18RR dataset.
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.