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arXiv · 2507.11954

The benefits of query-based KGQA systems for complex and temporal questions in LLM era

Artem Alekseev·Mikhail Chaichuk·Miron Butko·Alexander Panchenko·Elena Tutubalina·Oleg Somov
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Abstract

Large language models excel in question-answering (QA) yet still struggle with multi-hop reasoning and temporal questions. Query-based knowledge graph QA (KGQA) offers a modular alternative by generating executable queries instead of direct answers. We explore multi-stage query-based framework for WikiData QA, proposing multi-stage approach that enhances performance on challenging multi-hop and temporal benchmarks. Through generalization and rejection studies, we evaluate robustness across multi-hop and temporal QA datasets. Additionally, we introduce a novel entity linking and predicate matching method using CoT reasoning. Our results demonstrate the potential of query-based multi-stage KGQA framework for improving multi-hop and temporal QA with small language models. Code and data: https://github.com/ar2max/NLDB-KGQA-System

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Artem Alekseev, Mikhail Chaichuk, Miron Butko, Alexander Panchenko, Elena Tutubalina, Oleg Somov. 2025-07-16. The benefits of query-based KGQA systems for complex and temporal questions in LLM era. https://doi.org/10.1007/978-3-031-97141-9_29

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