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<p>The traditional semantic interpretation of oracle bone characters, the earliest known system of Chinese writing, has relied heavily on expert-driven manual analysis. Therefore, artificial intelligence (AI)-based approaches have been increasingly explored for deciphering these characters. A fundamental prerequisite for AI-driven semantic inference is construction of a high-quality evolutionary dataset. In this study, a graph-based evolutionary dataset is introduced, encompassing 756 groups and 3780 Chinese characters across five historical stages. Unlike existing datasets that primarily represent characters as images, the proposed dataset employs a graph-based representation, wherein nodes correspond to key structural points of a character, and edges define their spatial relationships. Experimental analyses demonstrate that graph representations offer superior capabilities in capturing the structural stability of characters across evolutionary stages compared to image-based representations. The dataset is expected to serve as a valuable resource for the application of AI-driven methodologies in the decipherment of unknown oracle bone characters.</p>

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A graph-based evolutionary dataset for oracle bone characters from inscriptions to modern Chinese scripts

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月26日

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  • Qingju Jiao,
  • Jingwen Wu,
  • Qi Liu,
  • Han Zhang,
  • Zhan Zhang,
  • Bang Li,
  • Jing Xiong,
  • Guoying Liu,
  • Yongge Liu

摘要

The traditional semantic interpretation of oracle bone characters, the earliest known system of Chinese writing, has relied heavily on expert-driven manual analysis. Therefore, artificial intelligence (AI)-based approaches have been increasingly explored for deciphering these characters. A fundamental prerequisite for AI-driven semantic inference is construction of a high-quality evolutionary dataset. In this study, a graph-based evolutionary dataset is introduced, encompassing 756 groups and 3780 Chinese characters across five historical stages. Unlike existing datasets that primarily represent characters as images, the proposed dataset employs a graph-based representation, wherein nodes correspond to key structural points of a character, and edges define their spatial relationships. Experimental analyses demonstrate that graph representations offer superior capabilities in capturing the structural stability of characters across evolutionary stages compared to image-based representations. The dataset is expected to serve as a valuable resource for the application of AI-driven methodologies in the decipherment of unknown oracle bone characters.