GRATE: Temporal Extensions for Inductive KG Foundation Models via Gated Rotary Attention
Knowledge graph foundation models such as Ultra and Trix achieve strong inductive transfer by learning relation-graph representations that generalise to unseen entities and relations. Extending this transferability to temporal knowledge graphs (TKGs) remains challenging: existing temporal models tie their parameters to dataset-specific entities, relations, or timestamps and are not designed to transfer to TKGs with disjoint vocabularies. We propose GRATE (Gated Rotary Attention for Temporal Encoding), an entity-side message function that adds no learnable parameters and encodes time through relative time differences by rotating each edge message according to its time gap to the query and ...
| Authors | Jiaxin Pan, Osama Mohammed, Daniel Hernández et al. |
|---|---|
| Primary category | cs.AI |
| Published | 11 July 2026 |
| Last updated at source | 11 July 2026 |
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