Fully Hyperbolic Rotation for Knowledge Graph Embedding
This work addresses knowledge graph completion, a key task in AI for structured data representation, with an incremental improvement over existing hyperbolic methods.
The authors tackled the problem of knowledge graph embedding by proposing a fully hyperbolic model that defines relations as Lorentz rotations directly in hyperbolic space, achieving competitive results with fewer parameters and state-of-the-art performance on CoDEx-s and CoDEx-m datasets.
Hyperbolic rotation is commonly used to effectively model knowledge graphs and their inherent hierarchies. However, existing hyperbolic rotation models rely on logarithmic and exponential mappings for feature transformation. These models only project data features into hyperbolic space for rotation, limiting their ability to fully exploit the hyperbolic space. To address this problem, we propose a novel fully hyperbolic model designed for knowledge graph embedding. Instead of feature mappings, we define the model directly in hyperbolic space with the Lorentz model. Our model considers each relation in knowledge graphs as a Lorentz rotation from the head entity to the tail entity. We adopt the Lorentzian version distance as the scoring function for measuring the plausibility of triplets. Extensive results on standard knowledge graph completion benchmarks demonstrated that our model achieves competitive results with fewer parameters. In addition, our model get the state-of-the-art performance on datasets of CoDEx-s and CoDEx-m, which are more diverse and challenging than before. Our code is available at https://github.com/llqy123/FHRE.