SABET-QA: Temporal Knowledge Graph Question Answering

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.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2608.20083 [cs.CL]

(or arXiv:2608.20083v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.20083 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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