Knowledge Graph Completion with Mixed Geometry Tensor Factorization
Abstract
In this paper, we propose a new geometric approach for knowledge graph completion via low rank tensor approximation. We augment a pretrained and well-established Euclidean model based on a Tucker tensor decomposition with a novel hyperbolic interaction term. This correction enables more nuanced capturing of distributional properties in data better aligned with real-world knowledge graphs. By combining two geometries together, our approach improves expressivity of the resulting model achieving new state-of-the-art link prediction accuracy with a significantly lower number of parameters compared to the previous Euclidean and hyperbolic models.
Cite
Text
Yusupov et al. "Knowledge Graph Completion with Mixed Geometry Tensor Factorization." Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, 2025.Markdown
[Yusupov et al. "Knowledge Graph Completion with Mixed Geometry Tensor Factorization." Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, 2025.](https://mlanthology.org/aistats/2025/yusupov2025aistats-knowledge/)BibTeX
@inproceedings{yusupov2025aistats-knowledge,
title = {{Knowledge Graph Completion with Mixed Geometry Tensor Factorization}},
author = {Yusupov, Viacheslav and Rakhuba, Maxim and Frolov, Evgeny},
booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics},
year = {2025},
pages = {4924-4932},
volume = {258},
url = {https://mlanthology.org/aistats/2025/yusupov2025aistats-knowledge/}
}