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Biquaternionic Space with Complex-Valued Attention for Temporal Knowledge Graph Completion
A new arXiv preprint proposes embedding temporal knowledge graphs in biquaternionic space, arguing that relying on a single geometric space limits how well models capture varied relational patterns. The method pairs that representation with complex-valued attention to score facts whose validity changes over time. The work targets the link-prediction task of inferring missing facts in evolving knowledge graphs.