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<p>Ancient Chinese costumes are a key component of China’s cultural heritage. This study introduces an intelligent question answering (Q&amp;A) system based on a domain-specific knowledge graph to enhance the accuracy of information retrieval. The proposed system integrates modules for named entity recognition (NER), question classification (QC), and recall and ranking. Experimental results indicate that the system achieves an F1 score of 88% for queries with explicit attribute values, and 80% for queries without explicit attributes, outperforming existing Q&amp;A system architectures. To further improve NER performance in complex contexts, we propose the RoBERTa-BiLSTM-SDPA-CRF model, which achieves F1 scores of 92% on a proprietary dataset and 81% on a public dataset. Additionally, the system incorporates both text- and image-based responses, enriching user interaction. This research contributes to the advancement of domain-specific knowledge retrieval and fosters the dissemination of cultural heritage by facilitating a more comprehensive understanding of Ancient Chinese costumes.</p>

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Knowledge graph-based intelligent question answering system for ancient Chinese costume heritage

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月21日

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  • Hua Yuan,
  • Yuhan Li,
  • Baohui Wang,
  • Kaixuan Liu,
  • Junjie Zhang

摘要

Ancient Chinese costumes are a key component of China’s cultural heritage. This study introduces an intelligent question answering (Q&A) system based on a domain-specific knowledge graph to enhance the accuracy of information retrieval. The proposed system integrates modules for named entity recognition (NER), question classification (QC), and recall and ranking. Experimental results indicate that the system achieves an F1 score of 88% for queries with explicit attribute values, and 80% for queries without explicit attributes, outperforming existing Q&A system architectures. To further improve NER performance in complex contexts, we propose the RoBERTa-BiLSTM-SDPA-CRF model, which achieves F1 scores of 92% on a proprietary dataset and 81% on a public dataset. Additionally, the system incorporates both text- and image-based responses, enriching user interaction. This research contributes to the advancement of domain-specific knowledge retrieval and fosters the dissemination of cultural heritage by facilitating a more comprehensive understanding of Ancient Chinese costumes.