Research on the Construction of Knowledge Graph of Intangible Cultural Heritage from the Perspective of AIGC

Authors

Yucheng CHEN, 1.National Science Library, Chinese Academy of Sciences, Beijing 100190 2.Department of Information Resourses Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190
LI Yang, 1.National Science Library, Chinese Academy of Sciences, Beijing 100190 2.Department of Information Resourses Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190
Jiangfeng LIU, 1.School of Information Management, Nanjing University, Nanjing 210023 2.Laboratory of Data Intelligence and Cross Innovation, Nanjing University, Nanjing 210023
Fan YANG

Keywords

LLM; AIGC; intangible cultural heritage; knowledge graph

Abstract

[Purpose/significance]Intangible cultural heritage is an important component of human civilization, which is of great significance for protecting and promoting national spirit, enhancing national identity and cohesion. [Method/process]This paper explores how to utilize the advantages of  AIGC, combined with traditional deep learning methods, then construct a comprehensive and efficient map of intangible cultural heritage knowledge. [Result/conclusion]In the classification study of intangible cultural heritage projects, the fine-tuned Baihuan-7B has the best effect, with an macro-F1 value of 0.7688. In the extraction of intangible cultural heritage attribute information, RoBERTa has the best effect, with an F1 value of 0.7085. The 2-Gram BLEU generated by fine-tuning Baihuan-7B is 0.2052. Combining the results of attribute extraction and generation, an efficient and comprehensive knowledge graph is constructed. [Innovation/limitation]This study uses a generative large model to assist in the establishment of knowledge graphs, focus on national-level intangible fruit projects, not induding provinciul-level projects with high reasearch value.

First Page

115

Last Page

126

Submission Date

2023

Revision Date

2023

Publication Date

4-1-2024

Digital Object Identifier (DOI)

10.19809/j.cnki.kjqbyj.2023.03.006

Recommended Citation

CHEN, Yucheng; Yang, LI; LIU, Jiangfeng; and YANG, Fan (2024) "Research on the Construction of Knowledge Graph of Intangible Cultural Heritage from the Perspective of AIGC," Scientific Information Research: Vol. 6: Iss. 2, Article 10.
DOI: 10.19809/j.cnki.kjqbyj.2023.03.006
Available at: https://eng.kjqbyj.com/journal/vol6/iss2/10

Reference

[1] 国务院. 中共中央 国务院印发 《数字中国建设整体布局规划》 [EB/OL]. (2023-02-27) [2023-10-09].
https://www.gov.cn/zhengce/2023-02/27/content_5743484.htm.
[2] 王晓光. "文化遗产智慧数据资源建设与服务" 专题前言 [J/OL]. 信息资源管理学报, 1 [2024-01-15].
http://kns.cnki.net/kcms/detail/42.1812.G2.20230814.0859.008.html.
[3] 聂鑫. 非物质文化遗产的知识产权保护及其边界研究 [J]. 文化遗产, 2023(03): 24-33.
[4] 徐增林, 盛泳潘, 贺丽荣, 等. 知识图谱技术综述 [J]. 电子科技大学学报, 2016, 45(04): 589-606.
[5] UNESCO. Convention for the Safeguarding of the Intangible Cultural Heritage(2003) [EB/OL]. (2003-12-26) [2023-10-09].
https://www.ihchina.cn/zhengce_details/11667.
[6] 国务院办公厅. 国务院办公厅关于加强我国非物质文化遗产保护工作的意见 [EB/OL]. (2005-08-15) [2023-10-09].
http://www.gov.cn.tsinghua.yitlink.com:8443/zwgk/2005-08/15/content_21681.htm.
[7] 全国人民代表大会常务委员会. 中华人民共和国非物质文化遗产法 [EB/OL]. (2011-05-10) [2023-10-09].
http://www.npc.gov.cn/zgrdw/huiyi/lfzt/fwzwhycbhf/2011-05/10/content_1729844.htm.
[8] 国新网. 第五批国家级非物质文化遗产代表性项目名录国务院政策例行吹风会 [EB/OL]. (2021-06-10) [2023-10-09].
https://www.ihchina.cn/project_details/23037.
[9] 董坤. 基于知识元的非物质文化遗产知识抽取与组织研究 [J]. 情报理论与实践, 2021, 44(09): 155-160, 148.
[10] 庄文杰, 谈国新, 侯西龙, 等. 非物质文化遗产视频知识元组织模型研究 [J]. 情报科学, 2018, 36(12): 25-32.
[11] 侯西龙, 谈国新, 庄文杰, 等. 基于关联数据的非物质文化遗产知识管理研究 [J]. 中国图书馆学报, 2019, 45(02): 88-108.
[12] 曾子明, 周知, 蒋琳. 基于关联数据的数字人文视觉资源知识组织研究 [J]. 情报资料工作, 2018(06): 6-12.
[13] DOU J H, QIN J Y, JIN Z X, et al. Knowledge graph based on domain ontology and natural language processing technology for Chinese intangible cultural heritage [J]. Journal of Visual Languages & Computing, 2018, 48: 19-28.
[14] 赵朝阳, 朱贵波, 王金桥. ChatGPT给语言大模型带来的启示和多模态大模型新的发展思路 [J]. 数据分析与知识发现, 2023, 7(03): 26-35.
[15] OPENAI. ChatGPT: Optimizing language models for dialogue [EB/OL]. (2023-02-20) [2023-10-09]. https://openai.com/blog/chatgpt.
[16] 安波. 基于提示学习的小样本文献分类方法 [J/OL]. 图书馆论坛, 1-10 [2024-01-15]
http://kns.cnki.net/kcms/detail/44.1306.G2.20230620.1114.002.html.
[17] 张华平, 李林翰, 李春锦. ChatGPT中文性能测评与风险应对 [J]. 数据分析与知识发现, 2023, 7(03): 16-25.
[18] 王昀, 胡珉, 塔娜, 等. 大语言模型及其在政务领域的应用 [J/OL]. 清华大学学报(自然科学版): 1-10 [2023-08-28].
https://doi.org/10.16511/j.cnki.qhdxxb.2023.26.042.
[19] BOLLACKER K, EVANS C, PARITOSH P, et al. Freebase: a collaboratively created graph database for structuring human knowledge [C] //Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data. New York: ACM, 2008: 1247-1250.
[20] AUER S, BIZER C, KOBILAROV G, et al. DBpedia: a Nucleus for a Web of Open Data [C] //6th International Semantic Web Conference, 2nd Asian Semantic Web ConferenceBusan: 2007: 722-735.
[21] BIZERC, LEHMANN J, KOBILAROV G, et al. DBpedia: a crystallization point for the Web of data [J]. Journal of Web Semantics, 2009, 7(03): 154-165.
[22] SUCHANEK F M, KASNECI G, WEIKUM G. Yago: A core of semantic knowledge [C] //Proceedings of the 16th International Conference on World Wide Web, New York: ACM, 2007: 697-706.
[23] SUCHANEK F M, KASNECI G, WEIKUM G. Yago:a large ontology from Wikipedia and WordNet [J]. Journal of Web Semantics, 2008, 6(03): 203-217.
[24] STUTZBACH, ALISA R. MusicBrainz [J]. Notes, 2011, 68(01).
[25] FERRUCCI D, BROWN E, CHU-CARROLL J, et al. Building Waston: An Overview of the deepQA Project [J]. AI Magazine, 2010, 31(03): 59-79.
[26] 陆伟, 戚越, 胡潇戈, 等. 图书馆自动问答系统的设计与实现 [J]. 情报工程, 2019, 5(02): 5-16.
[27] 马晨浩. 基于甲状腺知识图谱的自动问答系统的设计与实现 [J]. 智能计算机与应用, 2018, 8(03): 102-107.
[28] 窦小强. 基于军事知识图谱的问答系统 [C] //中国指挥与控制学会第六届中国指挥控制大会论文集(上册), 北京: 2018: 537-541.
[29] STROBELT H, WEBSON A, SANH V, et al. Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models [J]. IEEE Transactions on Visualization and Computer Graphics, 2023, 29(01):1146-1156.
[30] 潘雨黛, 张玲玲, 蔡忠闽, 等. 基于大规模语言模型的知识图谱可微规则抽取 [J]. 计算机科学与探索, 2023, 11(10): 2403-2412.
[31] 李源, 马新宇, 杨国利, 等. 面向知识图谱和大语言模型的因果关系推断综述 [J]. 计算机科学与探索, 2023, 17(10): 2358-2376.