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SABET-QA: Temporal Knowledge Graph Question Answering

2026-08-20 14:16:35
Brahim Touayouch, Mirette Moawad, Dmitry Akulov
arXiv_AI
           
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.

Abstract (translated)

URL

https://arxiv.org/abs/2608.20083

PDF

https://arxiv.org/pdf/2608.20083.pdf

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