AdaTKG equips temporal knowledge graph entities with per-entity memories updated via a single shared learnable exponential moving average, allowing online adaptation and better reasoning on evolving facts.
Temporal knowledge graph forecasting without knowledge using in-context learning.arXiv preprint arXiv:2305.10613, 2023a
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TransFIR enables reasoning on temporal knowledge graphs for emerging entities by clustering them into semantic groups and borrowing interaction histories from similar known entities, yielding 28.6% average MRR gains.
STK-Adapter adds Spatial-Temporal MoE, Event-Aware MoE, and Cross-Modality Alignment MoE to integrate evolving TKG graphs and event chains into LLMs, reducing information loss and improving extrapolation performance over prior methods.
citing papers explorer
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AdaTKG: Adaptive Memory for Temporal Knowledge Graph Reasoning
AdaTKG equips temporal knowledge graph entities with per-entity memories updated via a single shared learnable exponential moving average, allowing online adaptation and better reasoning on evolving facts.
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Inductive Reasoning for Temporal Knowledge Graphs with Emerging Entities
TransFIR enables reasoning on temporal knowledge graphs for emerging entities by clustering them into semantic groups and borrowing interaction histories from similar known entities, yielding 28.6% average MRR gains.
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STK-Adapter: Incorporating Evolving Graph and Event Chain for Temporal Knowledge Graph Extrapolation
STK-Adapter adds Spatial-Temporal MoE, Event-Aware MoE, and Cross-Modality Alignment MoE to integrate evolving TKG graphs and event chains into LLMs, reducing information loss and improving extrapolation performance over prior methods.