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<p>Chinese Painting and Calligraphy (ChP&amp;C), key elements of Chinese cultural heritage, hold rich historical and artistic value. Although Large Language Models (LLMs) excel in open-domain question answering (QA), they often suffer from hallucinations in domain-specific contexts. To address this, we propose a ChP&amp;C QA method integrating LLMs with a retrieval-augmented approach using a Knowledge Graph (KG) and external documents. The method decomposes complex questions into sub-questions and entities, retrieves relevant knowledge from KG and documents, and integrates answers via a dedicated module. We constructed a QA dataset focused on ChP&amp;C to validate the proposed method. Additionally, a QA system was developed to systematically evaluate its performance in real-world applications. Experimental results demonstrate improved semantic understanding and answer accuracy by effectively combining structured and unstructured information. This system offers a reliable tool for accessing ChP&amp;C knowledge and serves as a reference for intelligent QA in other cultural heritage domains.</p>

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An LLM-based QA system for Chinese Painting and Calligraphy with Knowledge Graphs and external documents

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月9日

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  • Jing Wan,
  • Xinrong Li,
  • Hao Zhang,
  • Ao Zou,
  • Rumei Wang

摘要

Chinese Painting and Calligraphy (ChP&C), key elements of Chinese cultural heritage, hold rich historical and artistic value. Although Large Language Models (LLMs) excel in open-domain question answering (QA), they often suffer from hallucinations in domain-specific contexts. To address this, we propose a ChP&C QA method integrating LLMs with a retrieval-augmented approach using a Knowledge Graph (KG) and external documents. The method decomposes complex questions into sub-questions and entities, retrieves relevant knowledge from KG and documents, and integrates answers via a dedicated module. We constructed a QA dataset focused on ChP&C to validate the proposed method. Additionally, a QA system was developed to systematically evaluate its performance in real-world applications. Experimental results demonstrate improved semantic understanding and answer accuracy by effectively combining structured and unstructured information. This system offers a reliable tool for accessing ChP&C knowledge and serves as a reference for intelligent QA in other cultural heritage domains.