Abstract
With the rapid development of natural language processing technology and knowledge graph technology, knowledge graph based intelligent question answering systems are being increasingly applied in various fields and industries. In the poultry industry, it is of great importance for farmers to promptly obtain scientific information of poultry disease diagnosis and curing measurements, where knowledge graph based question answering systems can contribute richly. In this paper, we design and implement Knowledge Graph based Chicken Disease Diagnosis Question Answering System (CDD-QAS) via deep learning models. We build a knowledge graph for chicken disease diagnosis, which contains 28 common chicken diseases and their corresponding symptoms, prevention and curing measures. In the construction of intelligent question answering system, we use BERT-TextCNN to realize the task of intention recognition and use BiLSTM-CRF to realize the task of entity recognition. Experimental results show that our proposed system can achieve better performance than other models, and possess great interactivity and accuracy. The proposed system can make great contribution to poultry industry and sets a good example of applying knowledge graph and deep learning methods in building question answering system.
This work was supported by Key Laboratory of Information System Requirements, No: LHZZ 2021-M04.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, H., Xiao, J., Gao, X., Wang, H.: Design and implementation of poultry disease diagnosis and information management system based on BP neural network optimized by genetic algorithm. Heilongjiang Anim. Sci. Vet. Med. 2, 1–4 (2017)
Zhou, Y.: Study on risk warning of poultry industry based on AHP-DS - a case study of Jiangxi Province. China Poultry 40(19), 60–63 (2018)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Kim, Y.: Convolutional neural networks for sentence classification (2014)
Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging. CoRR abs/1508.01991 (2015). https://arxiv.org/abs/1508.01991
Mbelwa, H., Machuve, D., Mbelwa, J.: Deep convolutional neural network for chicken diseases detection (2021)
Liu, Y., Li, J., Zhao, J.: Design and implementation of domain question-answering system based on semantic analysis. Comput. Appl. Softw. 38(11), 42–48 (2021)
Zhu, M., Ahuja, A., Wei, W., Reddy, C.K.: A hierarchical attention retrieval model for healthcare question answering. In: The World Wide Web Conference, pp. 2472–2482 (2019)
Mollá, D.: Macquarie university at BioASQ 6b: deep learning and deep reinforcement learning for query-based multi-document summarisation (2018)
Pappas, D., Stavropoulos, P., Androutsopoulos, I., McDonald, R.: BIOMRC: a dataset for biomedical machine reading comprehension. In: Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing, pp. 140–149 (2020)
Qi, G., Gao, H., Wu, T.: The research advances of knowledge graph. Technol. Intell. Eng. 3(1), 4–25 (2017)
Ion, R., et al.: An open-domain QA system for e-governance. arXiv preprint arXiv:2206.08046 (2022)
He, G., Lan, Y., Jiang, J., Zhao, W.X., Wen, J.R.: Improving multi-hop knowledge base question answering by learning intermediate supervision signals. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 553–561 (2021)
Akbari, H., Karaman, S., Bhargava, S., Chen, B., Vondrick, C., Chang, S.F.: Multi-level multimodal common semantic space for image-phrase grounding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12476–12486 (2019)
Qing Zhou, Y., Liu, X.J., Dong, Y.: Build a robust QA system with transformer-based mixture of experts. arXiv e-prints pp. arXiv-2204 (2022)
Faisal, F., Keshava, S., Anastasopoulos, A., et al.: SD-QA: spoken dialectal question answering for the real world. arXiv preprint arXiv:2109.12072 (2021)
Li, D., Hu, B., Chen, Q., Peng, W., Wang, A.: Towards medical machine reading comprehension with structural knowledge and plain text. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1427–1438 (2020)
Park, J., Cho, Y., Lee, H., Choo, J., Choi, E.: Knowledge graph-based question answering with electronic health records. In: Machine Learning for Healthcare Conference, pp. 36–53. PMLR (2021)
Kim, Y.: Convolutional neural networks for sentence classification. CoRR abs/1408.5882 (2014). https://arxiv.org/abs/1408.5882
Mikolov, T., Corrado, G., Chen, K., Dean, J.: Efficient estimation of word representations in vector space, pp. 1–12 (2013)
Wal, D., et al.: Biological data annotation via a human-augmenting AI-based labeling system. NPJ Digit. Med. 4 (2021). https://doi.org/10.1038/s41746-021-00520-6
Akkidas, D.: Viterbi algorithm. J. Exp. Algorithmics (2015)
Zhang, N., Jia, Q., Yin, K., Dong, L., Gao, F., Hua, N.: Conceptualized representation learning for Chinese biomedical text mining. In: WSDM 2020 Health Day (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gu, S. et al. (2022). Knowledge Graph Based Chicken Disease Diagnosis Question Answering System. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2022. Communications in Computer and Information Science, vol 1745. Springer, Singapore. https://doi.org/10.1007/978-981-19-8991-9_18
Download citation
DOI: https://doi.org/10.1007/978-981-19-8991-9_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-8990-2
Online ISBN: 978-981-19-8991-9
eBook Packages: Computer ScienceComputer Science (R0)