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SABET-QA: Temporal Knowledge Graph Question Answering

arXiv 2608.20083 ↗First posted 20 Aug 2026cs.CL cs.AIcited by 0

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Abstract (arXiv)

Question Answering over Temporal Knowledge Graphs (TKGQA) requires reasoning over time-sensitive facts, yet existing embedding-based methods struggle with multi-step queries due to single-pass reasoning pipelines. We propose SABET-QA, a framework that iteratively refines reasoning states across multiple hops via a bidirectional entity-temporal scoring mechanism and a slot-aware contextualization module that aligns question semantics with temporal KG embeddings. A differentiable working memory enables progressive hypothesis refinement, while auxiliary temporal boundaries serve as coarse supervision when available. Experiments on CronQuestions, Complex-CronQuestions, MultiTQ, and TimeQuestions demonstrate consistent improvements over strong baselines, particularly on complex multi-step temporal queries.

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References · 17

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  7. 2020Tensor Decompositions for temporal knowledge base completioncited by 115
  8. 2022UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graphcited by 103
  9. 2021Question Answering Over Temporal Knowledge Graphscited by 99
  10. 2021Complex Temporal Question Answering on Knowledge Graphscited by 69
  11. 2022Temporal Knowledge Graph Completion: A Surveycited by 61
  12. 2020Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocolscited by 30
5 more
  1. 2024A Survey on Temporal Knowledge Graph: Representation Learning and Applicationscited by 30
  2. 2021TempoQR: Temporal Question Reasoning over Knowledge Graphscited by 26
  3. 2023Temporal Knowledge Question Answering via Abstract Reasoning Inductioncited by 23
  4. 2024Faithful Temporal Question Answering over Heterogeneous Sourcescited by 13
  5. 2024Temporal Knowledge Graph Question Answering: A Surveycited by 9

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