摘要
融合新一代AI技术,探索中国古代天象记录智慧数据的构建框架,提升天象记录的数据形态,进而提供丰富的数智化应用,深挖其作为世界上独特的天文观测数据资源和珍贵的中国传统文化遗产的现代价值。以古籍资源智慧性保护体系框架为指导,融合知识图谱和大语言模型技术,构建涵盖史料层、注解层、关联层、融合层和应用层的古代天象记录智慧数据构建框架HDAA-SD。选择史料中的天象记录及其相关学术研究文献,使用HDAA-SD框架构建古代天象记录的智慧数据,并通过对话模型来检验HDAA-SD框架的应用效果。研究表明,HDAA-SD框架有助于古代天象记录升级为智慧数据,并能助力古代天象记录在数智环境的应用开发,为古代科技文献的智慧数据构建和智慧化应用提供参考。
The study explores the integration of the new generation of AI technology to develop a smart data construction framework for ancient Chinese celestial phenomenon records.It aims to enhance the data format of these records,thereby enabling richer digital and intelligent applications while uncovering their modern value as a globally unique astronomical resource and a precious aspect of traditional Chinese cultural heritage.Guided by the framework for the intelligent preservation of ancient book resources,the study incorporates knowledge graph and large language model technology to construct the HDAA-SD(Historical Data Annotation and Application-Smart Data)framework.The framework covers five core layers:the historical data layer,annotation layer,association layer,fusion layer,and application layer.Celestial phenomenon records,along with related academic literature,are selected to test the construction and practical application of smart data using the HDAA-SD framework.The research shows that the HDAA-SD framework can help upgrade ancient celestial phenomenon records to smart data,supporting their integration into intelligent digital environments.The framework also provides a reference for the intelligent digitization and application of ancient scientific and technological documents.
作者
唐振贵
罗锦坤
胡蓉
Tang Zhengui;Luo Jinkun;Hu Rong(School of Journalism and Cultural Communication, Guangxi University of Finance and Economics;New Engineering Industry College, Putian University;College of Teacher Education, Southwest University)
出处
《图书馆杂志》
北大核心
2025年第4期70-83,共14页
Library Journal
基金
广西财经学院2023年科研课题“面向文化遗产项目的数智传播技术分类适配框架研究”(项目编号:BS2023016)的研究成果之一。
关键词
古代天象
智慧数据
知识图谱
大语言模型
Ancient celestial phenomenon
Smart data
Knowledge graphs
Large language models
