Abstract
The knowledge graphs play a crucial role in the medical field. However, the knowledge graphs built for helping patients, especially Chinese patients, are rare. This paper built a user-friendly medical knowledge graphs-based automatic question answering system. Firstly, the knowledge graphs contain five entities, namely Disease, Drug, Symptom, Department, and Check is built. Over 20,000 nodes and 160,000 relationships are imported from the open-source datasets. Secondly, a question answering system is established based on the knowledge graphs. The input sentence from users is segmented. Then the Term Frequency-Inverse Document Frequency method is used to extract features. After that, the features are classified by the Naive Bayes model for querying the results from the knowledge graphs. The verification results indicate that the question answering system can recognize the input sentence well and return good results. In addition, we create a user-friendly Chinese interface to display results for users.
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