Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering

DOI: http://dx.doi.org/10.15439/2022R44

Citation: Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering, Vu Dinh Khoa, Shivani Agarwal, Gloria Jeanette Rincon Aponte, Nguyen Thi Hong Nga, Vijender Kumar Solanki, Ewa Ziemba (eds). ACSIS, Vol. 33, pages 285–288 (2022)

Full text

Abstract. In recent years, it has been great interest for Question Answering (QA) systems applied to many areas placing a high value on the community. The study and development of such QA systems through chatbot tools in medicine raise great needs for clinicians in their daily activities. Chatbots use the knowledge that could be retrieved from a database, but with limited inference capability. In this paper, we propose a new QA system based on Knowledge Graph (knowledge graph) for Traditional Medicine. Data of the knowledge graph is obtained from two sources including those from diagnostic of treatment diagrams and those collected on well-known medical websites through the Internet. The knowledge graph is then formed by combining the entities and relationships using the Named Entity Recognition (NER) model. Diagnosis is made via the node similarity algorithm in the knowledge graph for symptom identification. The effectiveness of the system is demonstrated through theoretical analysis and real-world experimental outcomes.

References

1. R EFERENCES X. Chen, S. Jia, and Y. Xiang, “A review: Knowledge reasoning over knowledge graph,” Expert Systems with Applications, vol. 141, p. 112948, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0957417419306669
2. D. Fensel, U. Şimşek, K. Angele, E. Huaman, E. Kärle, O. Panasiuk, I. Toma, J. Umbrich, and A. Wahler, “Introduction: what is a knowledge graph?” in Knowledge Graphs. Springer, 2020, pp. 1–10.
3. X. Zou, “A survey on application of knowledge graph,” Journal of Physics: Conference Series, vol. 1487, no. 1, p. 012016, mar 2020. [Online]. Available: https://doi.org/10.1088/1742-6596/1487/1/012016
4. X. Huang, J. Zhang, Z. Xu, L. Ou, and J. Tong, “A knowledge graph based question answering method for medical domain,” PeerJ Computer Science, vol. 7, p. e667, 2021.
5. T. Souza Costa, S. Gottschalk, and E. Demidova, “Event-qa: A dataset for event-centric question answering over knowledge graphs,” in Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020, pp. 3157–3164.
6. Z. Jia, A. Abujabal, R. Saha Roy, J. Strötgen, and G. Weikum, “Tequila: Temporal question answering over knowledge bases,” in Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018, pp. 1807–1810.
7. W. Wu, Z. Zhu, Q. Lu, D. Zhang, and Q. Guo, “Introducing external knowledge to answer questions with implicit temporal constraints over knowledge base,” Future Internet, vol. 12, no. 3, p. 45, 2020.
8. Z. Jiang, C. Chi, and Y. Zhan, “Research on medical question answering system based on knowledge graph,” IEEE Access, vol. 9, pp. 21 094–21 101, 2021.
9. D. N. Tien and H. P. Van, “Graph neural network combined knowledge graph for recommendation system,” in International Conference on Computational Data and Social Networks. Springer, 2020, pp. 59–70.
10. Y. Xie, “A tcm question and answer system based on medical records knowledge graph,” in 2020 International Conference on Computing and Data Science (CDS). IEEE, 2020, pp. 373–376.
11. A. Mansouri, L. S. Affendey, and A. Mamat, “Named entity recognition approaches,” International Journal of Computer Science and Network Security, vol. 8, no. 2, pp. 339–344, 2008.
12. T. S. Lee, S. M. Shin, and S. S. Kang, “Named entity recognition for patent documents based on conditional random fields,” KIPS Transactions on Software and Data Engineering, vol. 5, no. 9, pp. 419–424, 2016.
13. M. Wirtz and M. Kutschmann, “Analyse der beurteilerübereinstimmung für kategoriale daten mittels cohens kappa und alternativer maße,” Die Rehabilitation, vol. 46, no. 06, pp. 370–377, 2007.
14. J. J. Miller, “Graph database applications and concepts with neo4j,” in Proceedings of the southern association for information systems conference, Atlanta, GA, USA, vol. 2324, no. 36, 2013.
15. R. K. Sharma and M. Joshi, “An analytical study and review of open source chatbot framework, rasa,” International Journal of Engineering Research and, vol. 9, no. 06, 2020.
16. A. Jiao, “An intelligent chatbot system based on entity extraction using rasa nlu and neural network,” in Journal of Physics: Conference Series, vol. 1487, no. 1. IOP Publishing, 2020, p. 012014.
17. F. Holzschuher and R. Peinl, “Performance of graph query languages: comparison of cypher, gremlin and native access in neo4j,” in Proceedings of the Joint EDBT/ICDT 2013 Workshops, 2013, pp. 195–204.
18. W. Lu, J. C. M. Janssen, E. E. Milios, N. Japkowicz, and Y. Zhang, “Node similarity in the citation graph,” Knowledge and Information Systems, vol. 11, pp. 105–129, 2006.
19. X. Wang, Y. Jiang, N. Bach, T. Wang, Z. Huang, F. Huang, and K. Tu, “Automated concatenation of embeddings for structured prediction,” arXiv preprint https://arxiv.org/abs/2010.05006, 2020.
20. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint https://arxiv.org/abs/1810.04805, 2018.
21. D. Q. Nguyen and A. T. Nguyen, “Phobert: Pre-trained language models for vietnamese,” arXiv preprint https://arxiv.org/abs/2003.00744, 2020.
22. A. Conneau, K. Khandelwal, N. Goyal, V. Chaudhary, G. Wenzek, F. Guzmán, E. Grave, M. Ott, L. Zettlemoyer, and V. Stoyanov, “Unsupervised cross-lingual representation learning at scale,” arXiv preprint https://arxiv.org/abs/1911.02116, 2019.
23. M. Raux, D. Sartorius, Y. Le Manach, J.-S. David, B. Riou, and B. Vivien, “What do prehospital trauma scores predict besides mortality?” Journal of Trauma and Acute Care Surgery, vol. 71, no. 3, pp. 754–759, 2011.