arXiv Machine Learning
Learning Across Temporal Graph Representation Silos
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What Do Temporal Graph Learning Models Learn?
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TeRoR: Decoupled Temporal Rotation with Relational Circular Region for Temporal Knowledge Graph Embedding
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FITTER: Vocabulary-Agnostic Cross-Domain Inference on Temporal Knowledge Graphs
arXiv:2608. 10668v1 Announce Type: new Abstract: Temporal knowledge graphs are central to many uses of the Semantic Web, but existing completion methods assume the entities, relation names, and timestamps to be reasoned about are already known at training time, restricting each model to a single graph and vocabulary.
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HiTS-CL: A Continual Learning Framework for Long-Horizon Temporal Knowledge Graph Extrapolation
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Hugging Face Trending Papers
Aug 11
FITTER: Vocabulary-Agnostic Cross-Domain Inference on Temporal Knowledge Graphs
Temporal knowledge graphs are central to many uses of the Semantic Web, but existing completion methods assume the entities, relation names, and timestamps to be reasoned about are already known at training time, restricting each model to a single graph and vocabulary. We propose FITTER, the first fully-inductive structural model for temporal knowledge graph link prediction that supports cross-domain transfer: the inference graph may contain entirely unseen entities, relation names, and timestamps drawn from a different domain.