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Knowledge Question-Answering System Based on Knowledge Graph of Traditional Chinese Medicine

With the change of health concept and medical model, traditional Chinese medicine (TCM) has shown more and more unique advantages. Users expect to query TCM knowledge efficiently through online. At the same time, intelligent question-answering system based on knowledge graphs is gradually becoming a new trend of natural interaction between humans and machines. It uses knowledge graph as the source of answers, which can more accurately understand the user’s questions described in natural language, and return more precise answers according to the real intention of the user. This paper extracts the triples of TCM knowledge from the instructions of pharmacy database and the special database of TCM and then stores them in the Neo4j graph database. Based on this knowledge graph, the question-answering system of TCM is realized. For the natural sentences entered by users, the question-answering system first identifies the entities in the sentences, then combines TF-IDF and word vector to generate sentence vectors, matches the most similar question-answering template, searches the answers in the knowledge graph according to the semantics of the templates and the entities in the questions, and finally answers the questions according to the returned data. The system can accurately answer the main components and pharmaceutical enterprises of TCM, which diseases and symptoms can be treated by TCM and so on.