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arXiv — NLP / Computation & Language · · 3 min read

SABET-QA: Temporal Knowledge Graph Question Answering

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Computer Science > Computation and Language

arXiv:2608.20083 (cs)
[Submitted on 20 Aug 2026]

Title:SABET-QA: Temporal Knowledge Graph Question Answering

View a PDF of the paper titled SABET-QA: Temporal Knowledge Graph Question Answering, by Brahim Touayouch and 2 other authors
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Abstract: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.
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2608.20083 [cs.CL]
  (or arXiv:2608.20083v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2608.20083
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Brahim Touayouch [view email]
[v1] Thu, 20 Aug 2026 14:16:35 UTC (202 KB)
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