Development of Knowledge Graph for University Courses Management
Автор: Ismail Aliyu, A. F. D. Kana, Salisu Aliyue
Журнал: International Journal of Education and Management Engineering @ijeme
Статья в выпуске: 2 vol.10, 2020 года.
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The task of Allocating courses to lecturers in many tertiary institutions is done manually by typing using word processor application. Motivated by the widespread application of knowledge graphs in different domains, we present automated approach based on knowledge graph to address the problem of manual course allocation to, a task usually carried out at the beginning of every semester or academic year by departments in tertiary institutions. The development of knowledge graph in a way that enables easy manipulation and automatic generation of course allocation schedule is the core contribution of this paper. Rather than storing the data in relational database tables, the system stores data in a knowledge graph which is in RDF/XML format and refer to it to support intelligent knowledge services. In addition to automatic generation of course allocation schedule, another important feature of the system proposed in this paper is its ability to enable easy implementation of tasks similar to Question Answering that are very important to education administrators, which the existing manual approach does not provide. Testing of the proposed system reveals its ability to perform effectively. Our approach of using Knowledge graph offers advantages such as flexibility and security.
Course allocation, Knowledge graph, Resource Description Framework (RDF)
Короткий адрес: https://sciup.org/15017246
IDR: 15017246 | DOI: 10.5815/ijeme.2020.02.01
Список литературы Development of Knowledge Graph for University Courses Management
- P. Chen, Y. Lu, V. W. Zheng, X. Chen, and B. Yang, “KnowEdu : A System to Construct Knowledge Graph for Education,” IEEE, vol. 6, pp. 31553–31563, 2018.
- Y. Chi, Y. Qin, R. Song, and H. Xu, “Knowledge Graph in Smart Education : A Case Study of Entrepreneurship Scientific Publication Management,” MDPI/sustainability, 2018.
- H. Paulheim, “Knowledge Graph Refinement : A Survey of Approaches and Evaluation Methods,” Semant. Web, vol. 1, 2016.
- L. Ehrlinger and W. Wöß, “Towards a Definition of Knowledge Graphs,” in 12th international conference on semantic systems SEMANTiCS, 2016, vol. 1695, pp. 1–4.
- N. Noy, Y. Gao, A. Naratanan, A. Patterson, and J. Taylor, “Industry-scale Knowledge Graphs Lessons and Challenges,” QUEUE, ACM, pp. 1–28, 2019.
- Q. Cong, Z. Feng, F. Li, L. Zhang, G. Rao, and C. Tao, “Constructing Biomedical Knowledge Graph Based on SemMedDB and Linked Open Data,” in 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2018, pp. 1628–1631.
- Y. Wang, K. Zhang, Q. Dai, C. Peng, and K. Zhao, “Construction and Application of Knowledge Graph in Full-service Unified Data Center of Electric Power System.,” in IMMAEE, 2018.
- M. Zhao et al., “Construction of an Industrial Knowledge Graph for Unstructured Chinese Text Learning,” MDPI/applied Sci., pp. 1–22, 2019.
- T. Yu et al., “Knowledge graph for TCM health preservation : Design , construction , and applications,” Artif. Intell. Med. Elsevier, vol. 77, pp. 48–52, 2017.
- K. Sun, Y. Liu, Z. Guo, and C. Wang, “Visualization for Knowledge Graph Based on Educational Data,” Int. J. Softw. Informatics, vol. 10, no. 3, pp. 1–13, 2016.
- D. S. Chaplot, Y. Yang, J. Carbonell, and K. R. Koedinger, “Data-driven Automated Induction of Prerequisite Structure Graphs,” in 9th Interntional Conference on Educational Data Mining, 2016, pp. 318–323.
- C. Liang, J. Ye, Z. Wu, B. Pursel, and C. L. Giles, “Recovering Concept Prerequisite Relations from University Course Dependencies,” in 7th Symposium on Educational Advances in Artificial Intelligence (EAAI), 2017, pp. 4786–4791.
- Y. Yang, H. Liu, J. Carbonell, and W. Ma, “Concept Graph Learning from Educational Data,” in WSDM, 2015, pp. 1–1.
- H. Liu, W. Ma, Y. Yang, and J. Carbonell, “Learning Concept Graphs from Online Educational Data,” J. Artif. Intell. Res., vol. 55, pp. 1059–1090, 2016.
- S. Decker, P. Mitra, and S. Melnik, “Framework for the Semantic Web : An RDF Tutorial,” IEEE Internet Comput., pp. 68–73, 2000.
- Apache Jena, “Apache Jena,” 2019. [Online]. Available: https://jena.apache.org. [Accessed: 22-Nov-2019].
- W. Chen, W. Xiong, X. Yan, and W. Y. Wang, “Variational Knowledge Graph Reasoning,” in NAACL-HLT, 2018, pp. 1823–1832.
- Y. Lin, Z. Liu, M. Sun, Y. Liu, and X. Zhu, “Learning Entity and Relation Embeddings for Knowledge Graph Completion,” in 29th AAAI Conference on Artificial Intelligence, 2015, pp. 2181–2187.
- W. Xiong, T. Hoang, and W. Y. Wang, “DeepPath : A Reinforcement Learning Method for Knowledge Graph Reasoning,” in Conference on Empirical Methods in Natural Language Processing, 2017, pp. 564–573.
