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<p>Named entity recognition (NER) is essential for structuring knowledge in the field of intangible cultural heritage (ICH), supporting applications such as knowledge graph construction and cultural research. However, the lack of annotated datasets for ICH has limited progress in this area. To address this, we present a Chinese dataset specifically designed for NER tasks in the ICH domain, covering key entity categories such as heritage items, inheritors, and material. Additionally, we propose an NER model that integrates RoBERTa for feature representation, the Kolmogorov-Arnold Network (KAN) for extracting complex entity patterns, and a conditional random field (CRF) for sequence labeling. Experimental results demonstrate the model’s effectiveness in capturing the intricate semantic dependencies in ICH texts. The dataset and model contribute to improving entity recognition in the ICH domain, facilitating the structured representation of cultural heritage knowledge. This work provides a valuable resource for further research in information extraction, digital preservation, and cultural heritage studies.</p>

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A Chinese Named Entity Recognition Dataset for Intangible Cultural Heritage

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月3日

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  • Shiyun Long,
  • Wei Li

摘要

Named entity recognition (NER) is essential for structuring knowledge in the field of intangible cultural heritage (ICH), supporting applications such as knowledge graph construction and cultural research. However, the lack of annotated datasets for ICH has limited progress in this area. To address this, we present a Chinese dataset specifically designed for NER tasks in the ICH domain, covering key entity categories such as heritage items, inheritors, and material. Additionally, we propose an NER model that integrates RoBERTa for feature representation, the Kolmogorov-Arnold Network (KAN) for extracting complex entity patterns, and a conditional random field (CRF) for sequence labeling. Experimental results demonstrate the model’s effectiveness in capturing the intricate semantic dependencies in ICH texts. The dataset and model contribute to improving entity recognition in the ICH domain, facilitating the structured representation of cultural heritage knowledge. This work provides a valuable resource for further research in information extraction, digital preservation, and cultural heritage studies.