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| Optimizing Automatic Question Answering System Based on Disease Knowledge Graph |
Li He,Liu Jiayu( ),Li Shiyu,Wu Di,Jin Shuaiqi |
| School of Management, Jilin University, Changchun 130022, China |
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Abstract [Objective] This paper optimizes one existing question answering system, aiming to provide a more accurate disease knowledge query tool for the public. [Methods] Based on the disease knowledge graph, we obtained the disease symptom entities with the help of AC algorithm and semantic similarity calculation. Then, we categorized users’ questions with manual annotation and AC. Finally, we encapsulated the matched words into a dictionary, which was converted to database query language to retrieve relevant answers to the questions. [Results] We examined our new system with the Chinese medical question and answering data set. It had an average accuracy of 86.0% by answering five types of questions on COVID-19, which is higher than the existing Q&A system. [Limitations] There are many missing values of data on “checkup” and “infection”, which affects the performance of our new system. [Conclusions] The optimized automatic question answering system is an effective knowledge retrieval tool for epidemic related diseases.
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Received: 21 December 2020
Published: 08 March 2021
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| Fund:The work is supported by the National Natural Science Foundation of China（Grant No）(71974075);the Graduate Innovation Fund of Jilin University（Grant No）(101832020CX060) |
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Corresponding Authors:
Liu Jiayu
E-mail: liujiayu0426@163.com
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