The COVID-19 Question Answering System Based on Knowledge Graph

The COVID-19 that emerged at the end of 2019 is the biggest public health emergency encountered by human in the past 100 years. In the face of COVID-19, people need to get correct, comprehensive and clear information. However, traditional information retrieval methods only return a collection of related web pages, and users need to distinguish the authenticity from redundant and complicated information. Therefore, such information acquisition methods are inefficient and cannot serve users well. To meet the needs of users for related information, it is necessary to study the question answering system for the COVID-19. This paper studies and builds a COVID-19 question answering system based on knowledge graph. In the System, the question answering function is realized by template matching, which based on the Naive Bayes algorithm. For the input questions, the system firstly performs entity recognition, using entity type labeling combined with entity similarity matching to identify entities in the user's questions. Then the system predicts the user's question intention and use the trained question classifier to predict the category number. Finally Cypher is utilized to query graph database to generate and output the answer. The system implemented in this paper can help users quickly obtain the information they want and improve the user's information acquisition efficiency. The system can provide people convenient and fast ways of obtaining information about COVID-19, such as medical treatment, health, materials, prevention and control, scientific research, so as to help people take precautions against diseases and decrease the incidence of COVID-19.