收稿日期: 2024-07-11
录用日期: 2024-05-30
网络出版日期: 2024-09-23
基金资助
国家重点研发计划 (2022YFC3303500)
Educational resource content review method based on knowledge graph and large language model collaboration
Received date: 2024-07-11
Accepted date: 2024-05-30
Online published: 2024-09-23
数字教育资源自动化内容审查是教育信息化时代的迫切需求, 特别是对教育资源是否超标的适用性审查, 存在知识点难定位和难理解的问题. 针对这一需求, 提出了一种基于教育知识图谱和大语言模型(简称“大模型”)协同的教育资源内容审查方法. 具体地, 首先利用“本体”思想, 设计并构建一个面向中小学教育的知识图谱; 之后, 利用大模型在文本生成和排序任务上的优势, 设计基于教学内容生成和排序剪枝的知识定位方法; 最后, 通过教学内容核心知识子图与知识图谱教学路径的冲突检测, 实现超标教学内容识别. 实验结果表明, 所提出的方法可有效应对教育资源内容的超标知识审查任务, 为基于知识图谱及大语言模型协同的教育应用开辟新的技术路径.
刘佳 , 孙新 , 张宇晴 . 知识图谱与大语言模型协同的教育资源内容审查[J]. 华东师范大学学报(自然科学版), 2024 , 2024(5) : 57 -69 . DOI: 10.3969/j.issn.1000-5641.2024.05.006
Automated content reviews on digital educational resources are urgently in demand in the educational informatization era. Especially in the applicability review of whether educational resources exceed the standard, there are problems with knowledge which are easy to exceed national curriculum standards and difficult to locate. In response to this demand, this study proposed a review method for educational resources based on the collaboration of an educational knowledge graph and a large language model . Specifically, this study initially utilized the ontology concept to design and construct a knowledge graph for curriculum education in primary and secondary schools. A knowledge localization method was subsequently designed based on teaching content generation, sorting, and pruning, by utilizing the advantages of large language models for text generation and sorting tasks. Finally, by detecting conflicts between the core knowledge sub-graph of teaching content and the knowledge graph teaching path, the goal of recognizing teaching content that exceeded the national standard was achieved. Experimental results demonstrate that the proposed method effectively addresses the task of reviewing exceptional standard knowledge in educational resource content. This opens up a new technological direction for educational application based on the knowledge graph and large language model collaboration.
1 | 教育部办公厅. 国家智慧教育平台数字教育资源内容审核规范(试行) [A/OL]. (2022-05-26) [2024-05-22]. http://www.moe.gov.cn/srcsite/A16/s3342/202211/t20221108_979699.html. |
2 | 邓志鸿, 唐世渭, 张铭, 等.. Ontology研究综述. 北京大学学报(自然科学版), 2002, (5): 038. |
3 | CHEN X, JIA S, XIANG Y.. A review: Knowledge reasoning over knowledge graph. Expert Systems with Applications, 2020, 141, 112948. |
4 | 沈红叶, 肖婉, 季一木, 等.. 教育知识图谱的类型、应用及挑战. 软件导刊, 2023, 22 (10): 237- 243. |
5 | 李艳燕, 张香玲, 李新, 等.. 面向智慧教育的学科知识图谱构建与创新应用. 电化教育研究, 2019, 40 (8): 60- 69. |
6 | 马富龙, 张泽琳, 闫燕.. 学科知识图谱: 内涵、技术架构、应用与发展趋势. 软件导刊, 2024, 23 (3): 212- 220. |
7 | ZAREMBA W, SUTSKEVER I, VINYALS O. Recurrent neural network regularization [EB/OL]. (2014-09-08)[2024-05-22]. https://arxiv.org/pdf/1409.2329. |
8 | GRAVES A. Long short-term memory [J]. Supervised Sequence Labelling with Recurrent Neural Networks, 2012: 37-45. |
9 | ILI? S, MARRESE-TAYLOR E, BALAZS J A, et al. Deep contextualized word representations for detecting sarcasm and irony [C]// ALEXANDRA B, SAIF M M, VERONIQUE H, et al. Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Brussels: Association for Computational Linguistics, 2018: 2-7. |
10 | VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need [EB/OL]. (2017-06-12)[2024-05-22]. https://arxiv.org/pdf/1706.03762. |
11 | DEVLIN J, CHANG M W, LEE K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding [EB/OL]. (2018-10-11)[2024-05-22]. https://arxiv.org/pdf/1810.04805. |
12 | FEDUS W, ZOPH B, SHAZEER N.. Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity. Journal of Machine Learning Research, 2022, 23 (120): 1- 39. |
13 | CHOWDHERY A, NARANG S, DEVLIN J, et al.. Palm: Scaling language modeling with pathways. Journal of Machine Learning Research, 2023, 24 (240): 1- 113. |
14 | RAE J W, BORGEAUD S, CAI T, et al. Scaling language models: Methods, analysis & insights from training gopher [EB/OL]. (2021-12-02)[2024-05-22]. https://arxiv.org/pdf/2112.11446. |
15 | 黄勃, 吴申奥, 王文广, 等. 图模互补: 知识图谱与大模型融合综述 [J/OL]. (2024-05-17)[2024-05-29]. 武汉大学学报(理学版), 2024: 397-412. https://doi.org/10.14188/j.1671-8836.2024.0040. |
16 | LEI Y , UREN V , MOTTA E . SemSearch: A search engine for the semantic web [M]// STEFFEN S, VOJTěCH S. Managing Knowledge in a World of Networks. Berlin: Springer, 2006: 238-245. |
17 | 季慧娟. K12教育知识图谱管理系统设计与实现 [D]. 武汉: 华中师范大学, 2021. |
18 | 付雷杰, 曹岩, 白瑀, 等.. 国内垂直领域知识图谱发展现状与展望. 计算机应用研究, 2021, 38 (11): 3201- 3214. |
19 | DU Z, QIAN Y, LIU X, et al. Glm: General language model pretraining with autoregressive blank infilling [C]// SMARANDA M, PRESLAV N, ALINE V. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Dublin: Association for Computational Linguistics. 2021: 320–335. |
20 | ACHIAM J, ADLER S, AGARWAL S, et al. Gpt-4 technical report [EB/OL]. (2023-03-15)[2024-06-30]. https://arxiv.org/pdf/2303.08774. |
/
〈 |
|
〉 |