Knowledge Graph Completion with Mixed Geometry Tensor Factorization
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908299391139840 |
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| author | Yusupov, Viacheslav Rakhuba, Maxim Frolov, Evgeny |
| author_facet | Yusupov, Viacheslav Rakhuba, Maxim Frolov, Evgeny |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02589 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Knowledge Graph Completion with Mixed Geometry Tensor Factorization Yusupov, Viacheslav Rakhuba, Maxim Frolov, Evgeny Machine Learning Artificial Intelligence Information Retrieval 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. |
| title | Knowledge Graph Completion with Mixed Geometry Tensor Factorization |
| topic | Machine Learning Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2504.02589 |