INTEGRATING KNOWLEDGE BASED ON ONTOLOGY AND KNOWLEDGE GRAPH FOR CHATBOT IN EDUCATION QUERY
Main Article Content
Abstract
Online learning has become increasingly prevalent in recent years, allowing learners to utilize electronic devices and internet platforms for studying, accessing materials, and acquiring knowledge. This study proposes a model that integrates relational knowledge ontology, operators, and knowledge graphs to optimize the representation of subject knowledge combined with specifications of relationships between knowledge components. Based on the constructed knowledge base, the model solves knowledge query problems to meet the requirements for building intelligent learning support systems. The proposed solution is applied to develop a knowledge query support system for the Database course in the form of a chatbot. This chatbot can facilitate querying course knowledge content according to knowledge classification and types of exercises within the course. The constructed system is evaluated and compared with current large language models in terms of supporting the study of the Database course.
Keywords
chatbot, database, intelligent system, knowledge-based systems, knowledge engineering
Article Details
References
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Truong, D. M., Nguyen, H. D., Vu, S., Pham, V. T., & Nguyen, D. (2022). Construct an intelligent querying system in education based on ontology integration. In Proceedings of the 2nd IEEE International Conference on Computing (ICOCO 2022), (pp. 340-345). IEEE. https://doi.org/10.1109/ICOCO56118.2022.10031735
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Agrawal, M., Hegselmann, S., Lang, H., Kim, Y., & Sontag, D. (2022). Large language models are few-shot clinical information extractors. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022), (pp. 1998-2022). https://doi.org/10.18653/v1/2022.emnlp-main.130
Akhter, S., Javed, M. K., Shah, S. Q., & Javaid, A. (2021). Highlighting the advantages and disadvantages of E-learning. Psychol. Educ, 58(5), 1607-1614.
Almeida, M., Ferreira, J., & Ferreira, F. A. (2019). Developing a multi-criteria decision support system for evaluating knowledge transfer by higher education institutions. Knowledge Management Research & Practice, 17(4), 358-372. https://doi.org/10.1080/14778238.2018.1534533
Angles, R., & Gutiérrez, C. (2008). Survey of graph database models. ACM Comp. Surv., 40(1), Article 1. https://doi.org/10.1145/1322432.1322433
Dale, R. (2016). The return of the chatbots. Natural Language Engineering, 22(5), 811-817. https://doi.org/10.1017/S1351324916000243
Davies, J. N., Verovko, M., Verovko, O., & Solomakha, I. (2020). Personalization of e-learning process using AI-powered chatbot integration. In Proceedings of International scientific-practical conference, MODS 2020, (pp. 209-216). Springer. https://doi.org/10.1007/978-3-030-58124-4_20
Do, N. V., Nguyen, H. D., & Hoang, L. N. (2020). Some techniques for intelligent searching on ontology-based knowledge domain in e-learning. In Proceedings of 12th International Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020), (vol. 2, pp. 313-320). https://doi.org/10.5220/0010174403130320
Elayyan, S. (2021). The future of education according to the fourth industrial revolution. Journal of Educational Technology and Online Learning, 4(1), 23-30. https://doi.org/10.31681/jetol.737193
Elmasri, R., & Navathe, S. B. (2016). Fundamentals of Database Systems. Global Edition.
Foster, E., & Godbole, S. (2022). Database systems: a pragmatic approach. Taylor & Francis.
Følstad, A., & Brandtzaeg, P. (2017). Chatbots – The new world of HCI. ACM Interactions, 24, 38-42. https://doi.org/10.1145/3085558
Guemmat, K., & Ouahabi, S. (2018). A literature review of indexing and searching techniques implementation in educational search engines. International Journal of Information and Communication Technology Education (IJICTE), 14(2), 72-83. https://doi.org/10.4018/IJICTE.2018040106
Hagedorn, T., Bone, M., Kruse, B., Grosse, I., & Blackburn, M. (2020). Knowledge representation with ontologies and semantic web technologies to promote augmented and artificial intelligence in systems engineering. Insight, 23(1), 15-20. https://doi.org/10.1002/inst.12279
Hogan, A., Blomqvist, E., Cochez, M., D’Amato, C., De Melo, G., Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., Ngomo, A.-C. N., Polleres, A., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., & Zimmermann, A. (2020). Knowledge graphs. ACM Computing Surveys, 54(4), Article 71. https://doi.org/10.1145/3447772
Kusairi, S. (2020). A Web-Based Formative Feedback System Development by Utilizing Isomorphic Multiple Choice Items to Support Physics Teaching and Learning. Journal of Technology and Science Education, 10(1), 117-126. https://doi.org/10.3926/jotse.781
Liu, Q., Huang, Z., Huang, Z., Liu, C., Chen, E., Su, Y., & Hu, G. (2018). Finding similar exercises in online education systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2018), (pp. 1821-1830). https://doi.org/10.1145/3219819.3219960
Nguyen, H., Do, N., Tran, N., Pham, H. (2018). Criteria of a Knowledge model for an Intelligent Problems Solver in Education. In Proceedings of 10th IEEE International Conference on Knowledge and Systems Engineering (KSE 2018), (pp. 288-293). https://doi.org/10.1109/KSE.2018.8573434
Nguyen, H. D., Do, N. V., Pham, V. T., Selamat, A., & Herrera-Viedma, E. (2020). A method for knowledge representation to design Intelligent Problems Solver in mathematics based on Rela-Ops model. IEEE Access, 8, 76991-77012. https://doi.org/10.1109/ACCESS.2020.2988929
Nguyen, H., Tran, T. V., Pham, X. T., Huynh, A. T., & Do, N. V. (2021). Ontology-based integration of knowledge base for building an intelligent searching chatbot. Sensors & Materials, 33(9), 3101-3123. https://doi.org/10.18494/SAM.2021.3264
Nguyen, H. D., Do, N. V., & Pham, V. T. (2022). A methodology for designing knowledge-based systems and applications. In Applications of Computational Intelligence in Multi-Disciplinary Research, (pp. 159-185). Academic Press. https://doi.org/10.1016/B978-0-12-823978-0.00001-0
Nguyen, H. D., Truong, D., Vu, S., Nguyen, D., Nguyen, H., & Tran, N. T. (2023). Knowledge Management for Information Querying System in Education via the Combination of Rela-Ops Model and Knowledge Graph. Journal of Cases on Information Technology (JCIT), 25(1), Article 13. http://doi.org/10.4018/JCIT.324113
Nguyen, H. (2023). Intelligent System in Education: Requirements and Design Method, Journal of Electronic Voltage and Application (JEVA), 4(2), 12-19. https://doi.org/10.30880/jeva.2023.04.02.002
Ngo, H. Q., Nguyen, H. D., & Le-Khac, N. A. (2024). Ontology Knowledge Map Approach Towards Building Linked Data for Vietnamese Legal Applications. Vietnam Journal of Computer Science, 11(02), 323-342. https://doi.org/10.1142/S2196888824500015
Noy, N., Gao, Y., Jain, A., Narayanan, A., Patterson, A., & Taylor, J. (2019). Industry-scale knowledge graphs: Lessons and challenges. Communications of the ACM, 62(8), 36-43. https://doi.org/10.1145/3331166
Okonkwo, C. W., & Ade-Ibijola, A. (2022). Chatbots applications in education: A systematic review. Computers and Education: Artificial Intelligence, 2, Article 100033. https://doi.org/10.1016/j.caeai.2021.100033
Silberschatz, A., Korth, H., & Sudarshan, S. (2019). Database System Concepts (7th ed.). McGraw-Hill.
Trinh, T. H., Wu, Y., Le, Q. V., He, H., & Luong, T. (2024). Solving olympiad geometry without human demonstrations. Nature, 625, 476-482. https://doi.org/10.1038/s41586-024-07115-7
Truong, D. M., Nguyen, H. D., Vu, S., Pham, V. T., & Nguyen, D. (2022). Construct an intelligent querying system in education based on ontology integration. In Proceedings of the 2nd IEEE International Conference on Computing (ICOCO 2022), (pp. 340-345). IEEE. https://doi.org/10.1109/ICOCO56118.2022.10031735
Weber, F., Wambsganss, T., Rüttimann, D., & Söllner, M. (2021). Pedagogical Agents for Interactive Lernaing: A Taxonomy of Conversational Agents in Education. Proceedings of 42nd International Conference on Information Systems (ICIS 2021) (pp. 1-17). Austin.
Yoo, S., & Jeong, O. (2020). Auto-growing knowledge graph-based intelligent chatbot using BERT. ICIC Express Lett, 14(1), 67-73. https://doi.org/10.24507/icicel.14.01.67
Zhang, F., & Li, X. (2024). Knowledge-enhanced online doctor recommendation framework based on knowledge graph and joint learning. Information Sciences, 662, Article 120268. https://doi.org/10.1016/j.ins.2024.120268