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Computer Science > Artificial Intelligence

arXiv:2305.04567 (cs)
[Submitted on 8 May 2023]

Title:Multi-source Education Knowledge Graph Construction and Fusion for College Curricula

Authors:Zeju Li, Linya Cheng, Chunhong Zhang, Xinning Zhu, Hui Zhao
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Abstract:The field of education has undergone a significant transformation due to the rapid advancements in Artificial Intelligence (AI). Among the various AI technologies, Knowledge Graphs (KGs) using Natural Language Processing (NLP) have emerged as powerful visualization tools for integrating multifaceted information. In the context of university education, the availability of numerous specialized courses and complicated learning resources often leads to inferior learning outcomes for students. In this paper, we propose an automated framework for knowledge extraction, visual KG construction, and graph fusion, tailored for the major of Electronic Information. Furthermore, we perform data analysis to investigate the correlation degree and relationship between courses, rank hot knowledge concepts, and explore the intersection of courses. Our objective is to enhance the learning efficiency of students and to explore new educational paradigms enabled by AI. The proposed framework is expected to enable students to better understand and appreciate the intricacies of their field of study by providing them with a comprehensive understanding of the relationships between the various concepts and courses.
Comments: accepted by ICALT2023
Subjects: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Information Retrieval (cs.IR)
Cite as: arXiv:2305.04567 [cs.AI]
  (or arXiv:2305.04567v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2305.04567
Journal reference: 2023 IEEE International Conference on Advanced Learning Technologies (ICALT)
Related DOI: https://doi.org/10.1109/ICALT58122.2023.00111

Submission history

From: Zeju Li [view email]
[v1] Mon, 8 May 2023 09:25:41 UTC (314 KB)
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