mlsanityData to 14 Oct 2026

FITTER: Vocabulary-Agnostic Cross-Domain Inference on Temporal Knowledge Graphs

arXiv 2608.10668 ↗First posted 11 Aug 2026cs.AIcited by 0

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Abstract (arXiv)

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. FITTER represents each predicate by its interaction patterns with others and time through encodings of relative rather than absolute ordering; message-passing fuses local and global temporal context to produce vocabulary-agnostic embeddings. We prove the temporal encoding is time-shift invariant and evaluate FITTER on cross-domain, cross-graph transfer over six temporal knowledge graph benchmarks of diverse domains, granularities, and time spans. FITTER consistently outperforms inductive baselines without retraining, indicating that vocabulary-agnostic structural learning is a viable foundation for inference over the heterogeneous knowledge graphs of the Semantic Web.

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References · 25

  1. 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,703
  2. 2021Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Predictioncited by 275
  3. 2019Inductive Relation Prediction by Subgraph Reasoningcited by 239
  4. 2017Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphscited by 149
  5. 2018Learning Sequence Encoders for Temporal Knowledge Graph Completioncited by 145
  6. 2020Tensor Decompositions for temporal knowledge base completioncited by 115
  7. 2019Diachronic Embedding for Temporal Knowledge Graph Completioncited by 104
  8. 2023Towards Foundation Models for Knowledge Graph Reasoningcited by 86
  9. 2020Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networkscited by 84
  10. 2021Complex Temporal Question Answering on Knowledge Graphscited by 69
  11. 2021TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph Forecastingcited by 67
  12. 2021TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphscited by 59
13 more
  1. 2023InGram: Inductive Knowledge Graph Embedding via Relation Graphscited by 52
  2. 2020TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotationcited by 48
  3. 2023Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learningcited by 37
  4. 2021Meta-Knowledge Transfer for Inductive Knowledge Graph Embeddingcited by 32
  5. 2022Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoningcited by 30
  6. 2023GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Modelscited by 30
  7. 2024A Survey on Temporal Knowledge Graph: Representation Learning and Applicationscited by 30
  8. 2025TRIX: A More Expressive Model for Zero-shot Domain Transfer in Knowledge Graphscited by 24
  9. 2024History repeats Itself: A Baseline for Temporal Knowledge Graph Forecastingcited by 15
  10. 2023zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Modelscited by 13
  11. 2023HGE: Embedding Temporal Knowledge Graphs in a Product Space of Heterogeneous Geometric Subspacescited by 10
  12. 2024Chain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completioncited by 10
  13. 2025GraphOracle: Efficient Fully-Inductive Knowledge Graph Reasoning via Relation-Dependency Graphscited by 7

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