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Large language models excel in question-answering (QA) but 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: https://github.com/ar2max/NLDB-KGQA-System .

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The Benefits of Query-Based KGQA Systems for Complex and Temporal Questions in LLM Era

  • 会议论文

  • 发表日期：

    2026年

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  • Artem Alekseev,
  • Mikhail Chaichuk,
  • Miron Butko,
  • Alexander Panchenko,
  • Elena Tutubalina,
  • Oleg Somov

摘要

Large language models excel in question-answering (QA) but 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: https://github.com/ar2max/NLDB-KGQA-System .