Biquaternionic Space with Complex-valued Attention for Temporal Knowledge Graph Completion
cs.LG, cs.AI
Submitted: 2026-09-13
Updated: 2026-09-13
License: http://creativecommons.org/licenses/by/4.0/
The gist: Temporal knowledge graph embedding (TKGE) models infer missing facts in knowledge graphs that evolve over time.
Terminology
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.
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