Survey of Research on Curriculum Knowledge Graph Construction Techniques

doi:10.19678/j.issn.1000-3428.0069543

Abstract

Abstract: In the context of ongoing advancements in educational informatization, constructing precise and efficient curriculum knowledge graphs has become key to promoting personalized education development. As a structured knowledge representation model, curriculum knowledge graphs reveal complex relations between curriculum content and learning objectives to optimize the allocation of educational resources, and tailoring personalized learning paths for learners. This survey presents a discussion around the techniques used to construct curriculum knowledge graphs, starting with an explanation of the basic concepts; intrinsic connections; and significant differences among general, educational, and curriculum knowledge graphs. It then delves into the key technologies used for building curriculum knowledge graphs, covering aspects such as curriculum ontology design, entity extraction, and relation extraction, and provides a detailed analysis and summary of their evolution, key features, and limitations. Furthermore, it explores the application value of curriculum knowledge graphs in scenarios such as learning resource recommendation, learner behavior profile and modeling, and multimodal curriculum knowledge graph construction. Finally, it focuses on the challenges in constructing curriculum knowledge graphs, such as data diversity and heterogeneity, difficulties in quality evaluation, and the lack of cross-curriculum integration, and provides future-oriented insights based on cutting-edge technologies such as deep learning and Large Language Models (LLMs).

Key words: curriculum knowledge graph, knowledge graph construction, entity extraction, relation extraction, deep learning

摘要： 在教育信息化持续推进的背景下,构建精准且高效的课程知识图谱已成为推动教育个性化发展的关键任务之一。课程知识图谱作为一种结构化的知识表示模型,旨在揭示课程内容与学习目标之间的复杂关联关系,以优化教育资源配置,并为学习者定制个性化的学习路径。围绕课程知识图谱的构建技术进行探讨,首先阐述知识图谱、教育知识图谱、课程知识图谱的基本概念及其之间的内在联系与显著差异;其次深入剖析课程知识图谱构建的关键技术,涵盖课程本体设计、实体抽取、关系抽取等方面,并对其发展历程、特点及局限性展开详细分析与总结;再次,探讨课程知识图谱在学习资源推荐、学习者画像建模和多模态课程知识图谱构建等场景中的应用价值;最后,聚焦于课程知识图谱在构建过程中所面临的难题,如数据多样性和异构性、知识图谱质量难以评估以及多课程交叉融合不足等,从深度学习、大语言模型(LLM)等前沿技术的角度出发,对未来的发展趋势进行展望。

关键词: 课程知识图谱, 知识图谱构建, 实体抽取, 关系抽取, 深度学习

CLC Number:

TP391

SUN Lijun, MENG Fanjun, XU Xingjian. Survey of Research on Curriculum Knowledge Graph Construction Techniques[J]. Computer Engineering, 2025, 51(11): 1-21.

孙丽郡, 孟繁军, 徐行健. 课程知识图谱构建技术研究综述[J]. 计算机工程, 2025, 51(11): 1-21.

/ Recommend / Download Citations

URL: https://www.ecice06.com/EN/10.19678/j.issn.1000-3428.0069543

https://www.ecice06.com/EN/Y2025/V51/I11/1

References

[1] ZHAO Z G, LUO X, CHEN M J, et al. A survey of knowledge graph construction using machine learning[J]. Computer Modeling in Engineering[WT《Times New Roman》]& Sciences, 2024, 139(1): 225-257.
[2] 郑庆华, 董博, 钱步月, 等. 智慧教育研究现状与发展趋势[J]. 计算机研究与发展, 2019, 56(1): 209-224. ZHENG Q H, DONG B, QIAN B Y, et al. The state of the art and future tendency of smart education[J]. Journal of Computer Research and Development, 2019, 56(1): 209-224. (in Chinese)
[3] 王彩云, 郑增亮, 蔡晓琼, 等. 知识图谱在医学领域的应用综述[J]. 生物医学工程学杂志, 2023, 40(5): 1040-1044. WANG C Y, ZHENG Z L, CAI X Q, et al. Overview of the application of knowledge graphs in the medical field[J]. Journal of Biomedical Engineering, 2023, 40(5): 1040-1044. (in Chinese)
[4] 蒋川宇, 韩翔宇, 杨文蕊, 等. 医学知识图谱研究与应用综述[J]. 计算机科学, 2023, 50(3): 83-93. JIANG C Y, HAN X Y, YANG W R, et al. Survey of medical knowledge graph research and application[J]. Computer Science, 2023, 50(3): 83-93. (in Chinese)
[5] 穆维松, 刘天琪, 苗子溦, 等. 知识图谱技术及其在农业领域应用研究进展[J]. 农业工程学报, 2023, 39(16): 1-12. MU W S, LIU T Q, MIAO Z W, et al. Research progress on knowledge graph technology and its application in agriculture[J]. Transactions of the Chinese Society of Agricultural Engineering, 2023, 39(16): 1-12. (in Chinese)
[6] 唐闻涛, 胡泽林. 农业知识图谱研究综述[J]. 计算机工程与应用, 2024, 60(2): 63-76. TANG W T, HU Z L. Survey of agricultural knowledge graph[J]. Computer Engineering and Applications, 2024, 60(2): 63-76. (in Chinese)
[7] 陈烨, 周刚, 卢记仓. 多模态知识图谱构建与应用研究综述[J]. 计算机应用研究, 2021, 38(12): 3535-3543. CHEN Y, ZHOU G, LU J C. Survey on construction and application research for multi-modal knowledge graphs[J]. Application Research of Computers, 2021, 38(12): 3535-3543. (in Chinese)
[8] 张吉祥, 张祥森, 武长旭, 等. 知识图谱构建技术综述[J]. 计算机工程, 2022, 48(3): 23-37. ZHANG J X, ZHANG X S, WU C X, et al. Survey of knowledge graph construction techniques[J]. Computer Engineering, 2022, 48(3): 23-37. (in Chinese)
[9] 张西硕, 柳林, 王海龙, 等. 知识图谱中实体关系抽取方法研究[J]. 计算机科学与探索, 2024, 18(3): 574-596. ZHANG X S, LIU L, WANG H L, et al. Survey of entity relationship extraction methods in knowledge graphs[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(3): 574-596. (in Chinese)
[10] 陈华钧. 浅谈大模型时代的知识图谱技术栈[J]. 中国计算机学会通讯, 2023, 19(9): 46-51. CHEN H J. On technology stack’s knowledge map in the age of big model[J]. Communication of the CCF, 2023, 19(9): 46-51. (in Chinese)
[11] PAN J Z, RAZNIEWSKI S, KALO J C, et al. Large language models and knowledge graphs: opportunities and challenges[EB/OL].[2024-02-11]. https://arxiv.org/abs/2308.06374v1.
[12] 王鑫, 陈子睿, 王昊奋. 知识图谱与大语言模型协同模式探究[J]. 中国计算机学会通讯, 2023, 19(11): 10-17. WANG X, CHEN Z R, WANG H F. Exploration of collaborative patterns between knowledge graph and large language model[J]. Communications of the CCF, 2023, 19(11): 10-17. (in Chinese)
[13] PAN S R, LUO L H, WANG Y F, et al. Unifying large language models and knowledge graphs: a roadmap[J]. IEEE Transactions on Knowledge and Data Engineering, 2024, 36(7): 3580-3599.
[14] ZHANG D Z, YU Y H, DONG J H, et al. MM-LLMs: recent advances in multimodal large language models[EB/OL].[2024-02-11]. https://arxiv.org/abs/2401.13601v5.
[15] 舒文韬, 李睿潇, 孙天祥, 等. 大型语言模型: 原理、实现与发展[J]. 计算机研究与发展, 2024, 61(2): 351-361. SHU W T, LI R X, SUN T X, et al. Large language models: principles, implementation, and progress[J]. Journal of Computer Research and Development, 2024, 61(2): 351-361. (in Chinese)
[16] 李光明. 初中化学学科知识图谱的构建与可视化查询系统的实现[D]. 上海: 上海师范大学, 2020. LI G M. Construction of knowledge map of junior middle school chemistry and realization of visual query system[D]. Shanghai: Shanghai Normal University, 2020. (in Chinese)
[17] 田玲, 张谨川, 张晋豪, 等. 知识图谱综述——表示、构建、推理与知识超图理论[J]. 计算机应用, 2021, 41(8): 2161-2186. TIAN L, ZHANG J C, ZHANG J H, et al. Knowledge graph survey: representation, construction, reasoning and knowledge hypergraph theory[J]. Journal of Computer Applications, 2021, 41(8): 2161-2186. (in Chinese)
[18] 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. LI Y, MA X Y, YANG G L, et al. Survey of causal inference for knowledge graphs and large language models[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(10): 2358-2376. (in Chinese)
[19] YAN L X, SHA L L, ZHAO L X, et al. Practical and ethical challenges of large language models in education: a systematic scoping review[J]. British Journal of Educational Technology, 2024, 55(1): 90-112.
[20] WANG J Q, CHANG Y Y, LI Z, et al. TechGPT-2.0: a large language model project to solve the task of knowledge graph construction[EB/OL].[2024-02-11]. https://arxiv.org/abs/2401.04507v1.
[21] SYED M H, HUY T Q B, CHUNG S T. Context-aware explainable recommendation based on domain knowledge graph[J]. Big Data and Cognitive Computing, 2022, 6(1): 11.
[22] ABU-SALIH B, ALOTAIBI S. A systematic literature review of knowledge graph construction and application in education[J]. Heliyon, 2024, 10(3): 25383.
[23] 杨玉基, 许斌, 胡家威, 等. 一种准确而高效的领域知识图谱构建方法[J]. 软件学报, 2018, 29(10): 2931-2947. YANG Y J, XU B, HU J W, et al. Accurate and efficient method for constructing domain knowledge graph[J]. Journal of Software, 2018, 29(10): 2931-2947. (in Chinese)
[24] 周东岱, 董晓晓, 顾恒年. 教育领域知识图谱研究新趋向: 学科教学图谱[J]. 电化教育研究, 2024, 45(2): 91-97, 120. ZHOU D D, DONG X X, GU H N. A new trend of knowledge graph research in education: subject teaching graph[J]. e-Education research, 2024, 45(2): 91-97, 120. (in Chinese)
[25] DANG F R, TANG J T, PANG K Y, et al. Constructing an educational knowledge graph with concepts linked to Wikipedia[J]. Journal of Computer Science and Technology, 2021, 36(5): 1200-1211.
[26] SHEN Y L, CHEN Z H, CHENG G, et al. CKGG: a Chinese knowledge graph for high-school geography education and beyond[M]. Berlin, Germany: Springer International Publishing, 2021.
[27] ZHAO B W, SUN J D, XU B, et al. EDUKG: a heterogeneous sustainable K-12 educational knowledge graph[EB/OL].[2024-02-11]. https://arxiv.org/abs/2210.12228v1.
[28] 郑理欣. 面向计算机领域的多模态知识图谱构建方法研究[D]. 石家庄: 河北科技大学, 2022. ZHENG L X. Research on the construction method of multimodal knowledge map for computer field[D]. Shijiazhuang: Hebei University of Science and Technology, 2022. (in Chinese)
[29] 赵宇博, 张丽萍, 闫盛, 等. 个性化学习中学科知识图谱构建与应用综述[J]. 计算机工程与应用, 2023, 59(10): 1-21. ZHAO Y B, ZHANG L P, YAN S, et al. Construction and application of discipline knowledge graph in personalized learning[J]. Computer Engineering and Applications, 2023, 59(10): 1-21. (in Chinese)
[30] 董晓晓, 周东岱, 黄雪娇, 等. 学科核心素养发展导向下教育领域知识图谱模式构建方法研究[J]. 电化教育研究, 2022, 43(5): 76-83. DONG X X, ZHOU D D, HUANG X J, et al. Research on method of constructing knowledge graph mode in educational field oriented by subject core literacy[J]. e-Education Research, 2022, 43(5): 76-83. (in Chinese)
[31] CHETOUI I, EL BACHARI E, EL ADNANI M. Course recommendation model based on knowledge graph embedding[C]//Proceedings of the 16th International Conference on Signal-Image Technology[WT《Times New Roman》]& Internet-Based Systems (SITIS). Washington D.C., USA: IEEE Press, 2022: 510-514.
[32] KUMAR K, MANOCHA S. Constructing knowledge graph from unstructured text[J]. Self, 2015, 3: 4.
[33] BALTRUŠAITIS T, AHUJA C, MORENCY L P. Multimodal machine learning: a survey and taxonomy[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(2): 423-443.
[34] XU N, WANG J Y, TIAN Y, et al. AnANet: association and alignment network for modeling implicit relevance in cross-modal correlation classification[J]. IEEE Transactions on Multimedia, 2022, 25: 7867-7880.
[35] JIANG D, YE M. Cross-modal implicit relation reasoning and aligning for text-to-image person retrieval[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Washington D.C., USA: IEEE Press, 2023: 2787-2797.
[36] YANG X, TAO Q J, FENG X, et al. MMGA: multimodal learning with graph alignment[EB/OL].[2024-02-11]. https://arxiv.org/abs/2210.09946v2.
[37] ZHANG X, YUAN J, LI L, et al. Reducing the bias of visual objects in multimodal named entity recognition[C]//Proceedings of the 16th ACM International Conference on Web Search and Data Mining. New York, USA: ACM Press, 2023: 958-966.
[38] 高国伟, 王亚杰, 李永先. 我国知识元研究综述[J]. 情报科学, 2016, 34(2): 161-165. GAO G W, WANG Y J, LI Y X. Review of the research of domestic knowledge element[J]. Information Science, 2016, 34(2): 161-165. (in Chinese)
[39] 朱晓芸, 陈奇, 杨枨, 等. 决策支持系统中的广义知识元及模型库[C]//1993中国控制与决策学术年会论文集. 沈阳: 东北大学出版社, 1993: 4. ZHU X Y, CHEN Q, YANG C, et al. Generalized knowledge elements and model bases in decision support systems[C]//Proceedings of the 1993 China Control and Decision Academic Annual Conference. Shenyang: Northeastern University Press, 1993: 4. (in Chinese)
[40] 施江勇, 唐晋韬, 王勇军, 等. 基于知识图谱的新兴领域课程教学资源建设[J]. 高等工程教育研究, 2022(3): 15-20. SHI J Y, TANG J T, WANG Y J, et al. The construction of curriculum resources in emerging fields based on knowledge graph[J]. Research in Higher Education of Engineering, 2022(3): 15-20. (in Chinese)
[41] LI Z J, CHENG L Y, ZHANG C H, et al. Multi-source education knowledge graph construction and fusion for college curricula[C]//Proceedings of the IEEE International Conference on Advanced Learning Technologies (ICALT). Washington D.C., USA: IEEE Press, 2023: 359-363.
[42] 潘颖, 欧启忠, 肖耿毅. 面向语义的课程知识本体的构建[J]. 电化教育研究, 2007, 28(2): 19-21, 27. PAN Y, OU Q Z, XIAO G Y. Construction of semantic-oriented curriculum knowledge ontology[J]. e-Education Research, 2007, 28(2): 19-21, 27. (in Chinese)
[43] 邢科云. 课程知识本体的构建与应用研究[D]. 杭州: 杭州师范大学, 2010. XING K Y. Research on the construction and application of curriculum knowledge ontology[D]. Hangzhou: Hangzhou Normal University, 2010. (in Chinese)
[44] 姜强, 赵蔚, 王续迪. 自适应学习系统中用户模型和知识模型本体参考规范的设计[J]. 现代远距离教育, 2011(1): 61-65. JIANG Q, ZHAO W, WANG X D. Design of user model and knowledge model ontology reference specification in adaptive learning system[J]. Modern Distance Education, 2011(1): 61-65. (in Chinese)
[45] 詹川. 基于教育心理学的课程知识本体模型研究[J]. 图书情报工作, 2011, 55(14): 111-115. ZHAN C. E-learning course ontology model based on educational psychology[J]. Library and Information Service, 2011, 55(14): 111-115. (in Chinese)
[46] 黄焕, 元帅, 何婷婷, 等. 面向适应性学习系统的课程知识图谱构建研究——以"Java程序设计基础"课程为例[J]. 现代教育技术, 2019, 29(12): 89-95. HUANG H, YUAN S, HE T T, et al. Research on the construction of course knowledge graph for adaptive learning system—taking "Java programming foundation" course as an example[J]. Modern Educational Technology, 2019, 29(12): 89-95. (in Chinese)
[47] GAO J, WANG L, XU F. Research on the construction of course knowledge graph of high school information technology[C]//Proceedings of the International Conference on Artificial Intelligence and Education (ICAIE). Washington D.C., USA: IEEE Press, 2020: 211-215.
[48] YANG Z J, WANG Y, GAN J H, et al. Design and research of intelligent Question-Answering(Q[WT《Times New Roman》]&A) system based on high school course knowledge graph[J]. Mobile Networks and Applications, 2021, 26(5): 1884-1890.
[49] 宋丹, 胡瑛, 方正军, 等. 基于学情数据的智慧教学模式研究与实践[J]. 高等工程教育研究, 2022(6): 116-120. SONG D, HU Y, FANG Z J, et al. Research and practice of intelligent teaching mode based on learning situation data[J]. Research in Higher Education of Engineering, 2022(6): 116-120. (in Chinese)
[50] QAISER S, ALI R. Text mining: use of TF-IDF to examine the relevance of words to documents[J]. International Journal of Computer Applications, 2018, 181(1): 25-29.
[51] ZHU P, ZHONG W, YAO X M. Auto-construction of course knowledge graph based on course knowledge[J]. International Journal of Performability Engineering, 2019, 15(8): 2228.
[52] BAI J H, CHE L. Construction and application of database micro-course knowledge graph based on Neo4j[C]//Proceedings of the 2nd International Conference on Computing and Data Science. New York, USA: ACM Press, 2021: 1-5.
[53] ZHOU C Z. Academic new media service method based on knowledge graph[C]//Proceedings of the 2nd International Conference on Engineering Management and Information Science.[S. l.]: EAI Press, 2023: 1-9.
[54] THUSHARA M G, MOWNIKA T, MANGAMURU R. A comparative study on different keyword extraction algorithms[C]//Proceedings of the 3rd International Conference on Computing Methodologies and Communication (ICCMC). Washington D.C., USA: IEEE Press, 2019: 969-973.
[55] ZHANG M X, LI X M, YUE S B, et al. An empirical study of TextRank for keyword extraction[J]. IEEE Access, 2020, 8: 178849-178858.
[56] 陈曦, 梅广, 张金金, 等. 融合知识图谱和协同过滤的学生成绩预测方法[J]. 计算机应用, 2020, 40(2): 595-601. CHEN X, MEI G, ZHANG J J, et al. Student grade prediction method based on knowledge graph and collaborative filtering[J]. Journal of Computer Applications, 2020, 40(2): 595-601. (in Chinese)
[57] 张水晶, 陈建峡, 吴歆韵. 一种句袋注意力远程监督关系抽取方法[J]. 计算机应用与软件, 2022, 39(8): 193-203. ZHANG S J, CHEN J X, WU X Y. A novel distant supervision relation extraction approach based on sentence bag attention[J]. Computer Applications and Software, 2022, 39(8): 193-203. (in Chinese)
[58] LI H, GONG R R, ZHONG Z M, et al. Research on personalized learning path planning model based on knowledge network[J]. Neural Computing and Applications, 2023, 35(12): 8809-8821.
[59] MA X Q, XU T, WANG F S, et al. Research on the construction of curriculum knowledge graph based on GMM[C]//Proceedings of the 3rd International Conference on Computer Information and Big Data Applications. Wuhan, China: VDE Press, 2022: 1-4.
[60] CHEN P H, LU Y, ZHENG V W, et al. An automatic knowledge graph construction system for K-12 education[C]//Proceedings of the 5th Annual ACM Conference on Learning at Scale. New York, USA: ACM Press, 2018: 1-4.
[61] XU T, MA X, WANG F, et al. Construction and application of subject knowledge graph for basic education[EB/OL].[2024-02-11]. https://ceur-ws.org/Vol-3206/paper09.pdf#:~:text=This%20paper%20proposes%20to%20use%20SVM%20to%20identify,design%20and%20implement%20a%20subject%20Knowledge%20Graph%20system.
[62] SOCHER R, HUVAL B, MANNING C D, et al. Semantic compositionality through recursive matrix-vector spaces[C]// Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Philadelphia, USA: Association for Computational Linguistics, 2012: 1201-1211.
[63] SUNDERMEYER M, SCHLÜTER R, NEY H. LSTM neural networks for language modeling[EB/OL].[2024-02-11]. https://www.isca-archive.org/interspeech_2012/sundermeyer12_interspeech.pdf.
[64] CHUNG J, GULCEHRE C, CHO K, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[EB/OL].[2024-02-11]. https://arxiv.org/abs/1412.3555v1.
[65] CHEN P H, LU Y, ZHENG V W, et al. KnowEdu: a system to construct knowledge graph for education[J]. IEEE Access, 2018, 6: 31553-31563.
[66] ZHOU P, SHI W, TIAN J, et al. Attention-based bidirectional long short-term memory networks for relation classification[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Stroudsburg, USA: ACL Press, 2016: 207-212.
[67] HUANG Z H, XU W, YU K. Bidirectional LSTM-CRF models for sequence tagging[EB/OL].[2024-02-11]. https://arxiv.org/abs/1508.01991v1.
[68] LIU S, YANG H, LI J Y, et al. Preliminary study on the knowledge graph construction of Chinese ancient history and culture[J]. Information, 2020, 11(4): 186.
[69] DEVLIN J, CHANG M W, LEE K, et al. BERT: pre-training of deep bidirectional Transformers for language understanding[EB/OL].[2024-02-11]. https://arxiv.org/abs/1810.04805v2.
[70] WANG J N. Math-KG: construction and applications of mathematical knowledge graph[EB/OL].[2024-02-11]. https://arxiv.org/abs/2205.03772v1.
[71] AIN Q U, CHATTI M A, BAKAR K G C, et al. Automatic construction of educational knowledge graphs: a word embedding-based approach[J]. Information, 2023, 14(10): 526.
[72] SU Y, ZHANG Y. Automatic construction of subject knowledge graph based on educational big data[C]//Proceedings of the 3rd International Conference on Big Data and Education. New York, USA: ACM Press, 2020: 30-36.
[73] LI N, SHEN Q, SONG R, et al. MEduKG: a deep-learning-based approach for multi-modal educational knowledge graph construction[J]. Information, 2022, 13(2): 91.
[74] YANG P R, WANG H J, HUANG Y Z, et al. LMKG: a large-scale and multi-source medical knowledge graph for intelligent medicine applications[J]. Knowledge-Based Systems, 2024, 284: 111323.
[75] WANG S, SUN X, LI X, et al. GPT-NER: named entity recognition via large language models[EB/OL].[2024-02-11]. https://arxiv.org/abs/2304.10428.
[76] LI J Y, LI H, PAN Z, et al. Prompting ChatGPT in MNER: enhanced multimodal named entity recognition with auxiliary refined knowledge[C]//Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023. Stroudsburg, USA: ACL Press, 2023: 1-16.
[77] 寇嘉颖, 赵卫东, 柳先辉. 融合关系传递信息的双图文档级关系抽取方法[J]. 计算机科学, 2023, 50(12): 229-235. KOU J Y, ZHAO W D, LIU X H. Method of document level relation extraction based on fusion of relational transfer information using double graph[J]. Computer Science, 2023, 50(12): 229-235. (in Chinese)
[78] 李冬梅, 张扬, 李东远, 等. 实体关系抽取方法研究综述[J]. 计算机研究与发展, 2020, 57(7): 1424-1448. LI D M, ZHANG Y, LI D Y, et al. Review of entity relation extraction methods[J]. Journal of Computer Research and Development, 2020, 57(7): 1424-1448. (in Chinese)
[79] ZHANG N Y, XU X, TAO L K, et al. DeepKE: a deep learning based knowledge extraction toolkit for knowledge base population[EB/OL].[2024-02-11]. https://arxiv.org/abs/2201.03335v6.
[80] MANNING C, SURDEANU M, BAUER J, et al. The Stanford CoreNLP natural language processing toolkit[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations. Stroudsburg, USA: ACL Press, 2014: 55-60.
[81] ETZIONI O, FADER A, CHRISTENSEN J, et al. Open information extraction: the second generation[C]//Proceedings of the 32nd International Joint Conference on Artificial Intelligence. Washington D.C., USA: IEEE Press, 2011: 3-10.
[82] SUCHANEK F M, SOZIO M, WEIKUM G. SOFIE: a self-organizing framework for information extraction[C]//Proceedings of the 18th International Conference on World Wide Web. New York, USA: ACM Press, 2009: 631-640.
[83] SCHMITZ M, SODERLAND S, BART R, et al. Open language learning for information extraction[C]//Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Philadelphia, USA: ACL Press, 2012: 523-534.
[84] LI G, WANG H, LIU H. Knowledge graph construction for computer networking course group in secondary vocational school based on multi-source heterogeneous data[C]//Proceedings of the 12th International Conference on Information Technology in Medicine and Education (ITME). Washington D.C., USA: IEEE Press, 2022: 99-103.
[85] JIANG P, LU S Q, GU Z Y, et al. Construction of guidance graph in blended learning based on knowledge point extraction[C]//Proceedings of the IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference. Washington D.C., USA: IEEE Press, 2023: 1751-1755.
[86] QIN Y H, CAO H, XUE L Y. Research and application of knowledge graph in teaching: take the database course as an example[J]. Journal of Physics: Conference Series, 2020, 1607(1): 012127.
[87] QIAO L, YIN C T, CHEN H, et al. Automated construction of course knowledge graph based on China MOOC platform[C]//Proceedings of the IEEE International Conference on Engineering, Technology and Education. Washington D.C., USA: IEEE Press, 2019: 1-7.
[88] 王昊奋, 漆桂林, 陈华钧. 知识图谱: 方法、实践与应用[M]. 北京: 电子工业出版社, 2019. WANG H F, QI G L, CHEN H J. Knowledge graph[M]. Beijing: Publishing House of Electronics Industry, 2019. (in Chinese)
[89] LÜ Z H, YI K X, ZHOU W J, et al. A review of the knowledge extraction technology in knowledge graph[C]//Proceedings of the 41st Chinese Control Conference (CCC). Washington D.C., USA: IEEE Press, 2022: 4211-4218.
[90] SONG Z R, WAN L. Research of Chinese relation extraction based on BERT[C]//Proceedings of the IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA). Washington D.C., USA: IEEE Press, 2023: 841-845.
[91] XIA X Q, LI X C, CHU H P, et al. Research on knowledge extraction in knowledge graph construction[C]//Proceedings of the 3rd International Conference on Computer Vision and Data Mining (ICCVDM).[S. l.]: SPIE Press, 2023: 391-401.
[92] LI Y, QIU J C, GUI S J, et al. Analytics 2.0 for precision education driven by knowledge map[C]//Proceedings of the IEEE Frontiers in Education Conference (FIE). Washington D.C., USA: IEEE Press, 2022: 1-5.
[93] 刘森淼. 面向知识图谱的关系抽取算法研究[D]. 南京: 南京理工大学, 2021. LIU S M. Research on relation extraction algorithm for knowledge map[D]. Nanjing: Nanjing University of Science and Technology, 2021. (in Chinese)
[94] HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.
[95] SUN Q, ZHANG K, LÜ L S, et al. Joint extraction of entities and overlapping relations by improved graph convolutional networks[J]. Applied Intelligence, 2022, 52(5): 5212-5224.
[96] MINTZ M, BILLS S, SNOW R, et al. Distant supervision for relation extraction without labeled data[C]//Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP. Philadelphia, USA: ACL Press, 2009: 1003-1011.
[97] ZENG D J, LIU K, CHEN Y B, et al. Distant supervision for relation extraction via piecewise convolutional neural networks[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: ACL Press, 2015: 1753-1762.
[98] LIN Y K, SHEN S Q, LIU Z Y, et al. Neural relation extraction with selective attention over instances[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, USA: ACL Press, 2016: 2124-2133.
[99] CHEN Y, CHEN Z Y. Research on the key technology of course ontology construction[J]. Journal of Physics: Conference Series, 2022, 2330(1): 012012.
[100] KANNAN A V, FRADKIN D, AKROTIRIANAKIS I, et al. Multimodal knowledge graph for deep learning papers and code[C]//Proceedings of the 29th ACM International Conference on Information[WT《Times New Roman》]& Knowledge Management. New York, USA: ACM Press, 2020: 3417-3420.
[101] 刘昀抒, 申彦明, 齐恒, 等. 基于层次结构图的多跳知识图谱问答模型[J]. 计算机工程, 2024, 50(1): 101-109. LIU Y S, SHEN Y M, QI H, et al. Multi-hop knowledge base question answering model based on hierarchical structure graph[J]. Computer Engineering, 2024, 50(1): 101-109. (in Chinese)
[102] XU G W, JIA G Y, SHI L, et al. Personalized course recommendation system fusing with knowledge graph and collaborative filtering[J]. Computational Intelligence and Neuroscience, 2021(1): 9590502.
[103] ZHANG X M, LIU S, WANG H Y. Personalized learning path recommendation for E-learning based on knowledge graph and graph convolutional network[J]. International Journal of Software Engineering and Knowledge Engineering, 2023, 33(1): 109-131.
[104] 吴昊, 徐行健, 孟繁军. 课程资源的融合知识图谱多任务特征推荐算法[J]. 计算机工程与应用, 2021, 57(21): 132-139. WU H, XU X J, MENG F J. Knowledge graph-assisted multi-task feature-based course recommendation algorithm[J]. Computer Engineering and Applications, 2021, 57(21): 132-139. (in Chinese)
[105] 赵玲朗, 范佳荣, 赵一婷, 等. 基于知识图谱的学习者画像模型设计与应用——以"高中物理"课程为例[J]. 现代教育技术, 2021, 31(2): 95-101. ZHAO L L, FAN J R, ZHAO Y T, et al. The design and application of the learners’ portrait model based on knowledge mapping—taking the "high school physics" course as an example[J]. Modern Educational Technology, 2021, 31(2): 95-101. (in Chinese)
[106] DENG L Q, XU X S, REN Y. Analysis and prediction of network connection behavior anomaly based on knowledge graph features[C]//Proceedings of the 3rd International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT).[S. l.]: SPIE Press, 2023: 309-316.
[107] 王春雷, 王肖, 刘凯. 多模态知识图谱表示学习综述[J]. 计算机应用, 2024, 44(1): 1-15. WANG C L, WANG X, LIU K. Multimodal knowledge graph representation learning: a review[J]. Journal of Computer Applications, 2024, 44(1): 1-15. (in Chinese)
[108] FETTACH Y, GHOGHO M, BENATALLAH B. Knowledge graphs in education and employability: a survey on applications and techniques[J]. IEEE Access, 2022, 10: 80174-80183.
[109] ZHENG L Q, LONG M L, CHEN B D, et al. Promoting knowledge elaboration, socially shared regulation, and group performance in collaborative learning: an automated assessment and feedback approach based on knowledge graphs[J]. International Journal of Educational Technology in Higher Education, 2023, 20(1): 46.
[110] WANG C, XU S S. Construction of the evaluation index system of physical education teaching in colleges and universities based on scientific knowledge graph[J]. Mobile Information Systems, 2024, 20(1): 1-13.
[111] 郑庆华, 刘欢, 龚铁梁, 等. 大数据知识工程发展现状及展望[J]. 中国工程科学, 2023, 25(2): 208-220. ZHENG Q H, LIU H, GONG T L, et al. Development and prospect of big data knowledge engineering[J]. Strategic Study of CAE, 2023, 25(2): 208-220. (in Chinese)
[112] YU Y Q, LIAO M H, WU J H, et al. TextHawk: exploring efficient fine-grained perception of multimodal large language models[EB/OL].[2024-02-11]. https://arxiv.org/abs/2404.09204v1.
[113] YE Q H, XU H Y, YE J B, et al. mPLUG-Owl2: revolutionizing multi-modal large language model with modality collaboration[EB/OL].[2024-02-11]. https://arxiv.org/abs/2311.04257v2.
[114] 胡斌皓, 张建朋, 陈鸿昶. 基于生成式对抗网络和正类无标签学习的知识图谱补全算法[J]. 计算机科学, 2024, 51(1): 310-315. HU B H, ZHANG J P, CHEN H C. Knowledge graph completion algorithm based on generative adversarial network and positive and unlabeled learning[J]. Computer Science, 2024, 51(1): 310-315. (in Chinese)
[115] 马坤, 安敬民, 李冠宇. 动态聚合实体和关系上下文的知识图谱补全[J]. 计算机工程, 2023, 49(8): 77-84, 95. MA K, AN J M, LI G Y. Knowledge graph completion with dynamically aggregating context of entity and relation[J]. Computer Engineering, 2023, 49(8): 77-84, 95. (in Chinese)
[116] LIU Y B, WEN F, ZONG T, et al. Research on joint extraction method of entity and relation triples based on hierarchical cascade labeling[J]. IEEE Access, 2022, 11: 9789-9798.
[117] POPOVIC N, FÄRBER M. Few-shot document-level relation extraction[EB/OL].[2024-02-11]. https://arxiv.org/abs/2205.02048v2.
[118] WEI X, CUI X Y, CHENG N, et al. ChatIE: zero-shot information extraction via chatting with ChatGPT[EB/OL].[2024-02-11]. https://arxiv.org/abs/2302.10205v2.
[119] CARTA S, GIULIANI A, PIANO L, et al. Iterative zero-shot LLM prompting for knowledge graph construction[EB/OL].[2024-02-11]. https://arxiv.org/abs/2307.01128v1.

Please choose a citation manager

Content to export