GRATE: Temporal Extensions for Inductive KG Foundation Models via Gated Rotary Attention

• URL: http://arxiv.org/abs/2607.10197v1
• Date: Sat, 11 Jul 2026 08:22:17 GMT
• Title: GRATE: Temporal Extensions for Inductive KG Foundation Models via Gated Rotary Attention
• Abstract summary: We propose GRATE, an entity-side message function that adds no learnable parameters and encodes time through relative time differences.<n>GRATE integrates into NBFNet-style knowledge graph foundation models while preserving structural transferability.
• Score: 12.927354609914952
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 applying a query-conditioned gate to select temporally relevant signals. GRATE integrates into NBFNet-style KG foundation models while preserving structural transferability. Existing TKG benchmarks evaluate within shared train/test vocabularies and cannot directly test cross-dataset temporal transfer; we therefore construct GDELTIndT and WIKIIndT, inductive transfer benchmark suites with disjoint entities, relations, and timestamps spanning both interpolation and extrapolation. Across these benchmarks and held-out forecasting datasets, a single jointly pretrained GRATE checkpoint improves over the static base model in most settings.

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