Desarrollo de un módulo para gestionar la estructura de un grafo de conocimiento del sector educativo.
Resumen
Un grafo de conocimiento provee un conjunto de nodos interrelacionados para representar el conocimiento de forma organizada. Crear un grafo de conocimiento es una tarea complicada porque se requiere proporcionar elementos que permitan interrelacionar los nodos de forma adecuada. En este trabajo se muestra el desarrollo de un módulo que gestiona la estructura de un grafo de conocimiento. El grafo resultante permite representar sistemáticamente los temas (nodos) de un área disciplinar y establecer sus interrelaciones (flechas) con base en su complejidad. Para la construcción del módulo se utilizaron tecnologías de desarrollo web. Para comprobar la funcionalidad del módulo, se ha construido la estructura del grafo de conocimiento en un sistema gestión de conocimiento enfocado al área disciplinar de matemáticas.
Citas
An, B., Chen, B., Han, X., & Sun, L. (2018, jun). Accurate Text-Enhanced Knowledge Graph Representation Learning.Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , Volume 1 (Long Papers) New Orleans, Louisiana.
Chein, M., & Mugnier, M.-L. (2009). Introduction. In M. Chein & M.-L. Mugnier (Eds.), Graph-based Knowledge Representation: Computational Foundations of Conceptual Graphs (pp. 1-17). Springer London.
Córdova, F. M., & Gutiérrez, F. A. (2018). Knowledge Management System in Service Companies. Procedia Computer Science, 139, 392-400.
Duan, Y., Shao, L., & Hu, G. (2018). Specifying Knowledge Graph with Data Graph, Information Graph, Knowledge Graph, and Wisdom Graph. International Journal of Software Innovation, 6(2), 10-25.
Elfaki, A., Aljaedi, A., & Duan, Y. (2019, 8-13 July 2019). Mapping ERD to Knowledge Graph. 2019 IEEE World Congress on Services (SERVICES),
Gamlath, S., & Wilson, T. (2020). Dimensions of student-to-student knowledge sharing in universities. Knowledge Management Research & Practice, 1-15.
Helena, S., & Schurink, W. (2005). The Interface Between Knowledge Management And Human Resources: A Qualitative Study. South African Journal of Human Resource Management, 3.
Iaria, A., Schwarz, C., & Waldinger, F. (2018). Frontier Knowledge and Scientific Production: Evidence from the Collapse of International Science*. The Quarterly Journal of Economics, 133(2), 927-991.
Li, Y., Zhao, J., Yang, L., & Zhang, Y. (2019). Construction, Visualization and Application of Knowledge Graph of Computer Science Major Proceedings of the 2019 International Conference on Big Data and Education, London, United Kingdom.
Liu, H.-C., You, J.-X., Li, Z., & Tian, G. (2017). Fuzzy Petri nets for knowledge representation and reasoning: A literature review. Engineering Applications of Artificial Intelligence, 60, 45-56.
Muhammad, L., Garba, E., Oye, N., & Wajiga, G. (2019). Modeling Techniques for Knowledge Representation of Expert System: A Survey. Journal of Applied Computer Science & Mathematics, 13(2), 39-44.
Nararatwong, R., Kertkeidkachorn, N., & Ichise, R. (2020, 9-13 Dec. 2020). Knowledge Graph Visualization: Challenges, Framework, and Implementation. 2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE),
Page, R. D. M. (2019). Ozymandias: a biodiversity knowledge graph. PeerJ, 7, e6739.
Qi, J., Ding, L., & Lim, S. (2020). Ontology-based knowledge representation of urban heat island mitigation strategies. Sustainable Cities and Society, 52.
Qiu, Y., Qiao, Y., Yang, S., & Yang, J. (2020, 23-25 Oct. 2020). Tax-KG: Taxation Big Data Visualization System for Knowledge Graph. 2020 IEEE 5th International Conference on Signal and Image Processing (ICSIP),
Tippmann, E., Sharkey Scott, P., & Parker, A. (2017). Boundary Capabilities in MNCs: Knowledge Transformation for Creative Solution Development. Journal of Management Studies, 54(4), 455-482.
To, H. D., & Do, P. (2020, 12-14 Nov. 2020). Extracting triples from Vietnamese text to create knowledge graph. 2020 12th International Conference on Knowledge and Systems Engineering (KSE),
Verma, P., & Sood, S. K. (2017). Internet of Things-based student performance evaluation framework. Behaviour & Information Technology, 37(2), 102-119.
Wang, Y., Wang, Y., Sun, Y., Zhu, L., Lu, K., Fu, C. W., Sedlmair, M., Deussen, O., & Chen, B. (2018). Revisiting Stress Majorization as a Unified Framework for Interactive Constrained Graph Visualization. IEEE Trans Vis Comput Graph, 24(1), 489-499.
