Biquaternionic Space with Complex-valued Attention for Temporal Knowledge Graph Completion

Abstract:Temporal knowledge graph embedding (TKGE) models infer missing facts in knowledge graphs that evolve over time. Many existing models use a single geometric space, which can limit their ability to represent diverse relational patterns, or treat entity representations as static. We propose Biquaternionic Space with Complex-valued Attention (BSCA), a TKGE model that combines circular and hyperbolic rotations within a unified biquaternionic framework. A complex-valued attention mechanism adaptively fuses time-conditioned and relation-conditioned entity representations, allowing them to vary with temporal and relational context. Experiments on five benchmark datasets show competitive performance across datasets, with the largest improvement on GDELT: BSCA achieves an MRR of 52.1\%, compared with 38.1\% for the strongest baseline in our comparison.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.14279 [cs.LG]

(or arXiv:2609.14279v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.14279 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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