The benefits of query-based KGQA systems for complex and temporal questions in LLM era
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908451874013184 |
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| author | Alekseev, Artem Chaichuk, Mikhail Butko, Miron Panchenko, Alexander Tutubalina, Elena Somov, Oleg |
| author_facet | Alekseev, Artem Chaichuk, Mikhail Butko, Miron Panchenko, Alexander Tutubalina, Elena Somov, Oleg |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_11954 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The benefits of query-based KGQA systems for complex and temporal questions in LLM era Alekseev, Artem Chaichuk, Mikhail Butko, Miron Panchenko, Alexander Tutubalina, Elena Somov, Oleg Computation and Language Machine Learning 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 |
| title | The benefits of query-based KGQA systems for complex and temporal questions in LLM era |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2507.11954 |