现如今, 互联网中存在海量的医疗领域知识可以用于医疗病情诊断, 但传统的搜索引擎并无法根据病人的实际情况做出合理的判断, 无法满足使用需求. 因此, 本文主要开发基于知识图谱问答系统. 该系统面向医疗领域, 采用爬虫技术获取了大量医疗数据并将其存储在Neo4j图数据库构建医疗知识图谱中. 同时, 为了使系统能够进一步理解用户的医疗询问问句, 本文提出了基于BERT以及BERT-BiLSTM-CRF模型分别用于识别问句中的意图信息和实体信息的方法. 最后, 系统利用意图和实体信息在知识图谱中进行查询并为用户提供合适的回答, 完成了医疗问答系统的构建.
Nowadays, a large amount of medical domain knowledge on the Internet can be used for medical diagnosis, but traditional search engines cannot make reasonable judgments based on the actual situation of patients and fail to meet the needs of use. Therefore, this study mainly develops a question-answering system based on a knowledge graph. The system is applied to the medical field, which uses crawler technology to obtain a large amount of medical data and stores them in the constructed medical knowledge graph of the Neo4j graph database. At the same time, in order to enable the system to further understand the user’s medical questions, this study proposes methods based on BERT and BERT-BiLSTM-CRF models for identifying intent information and entity information in questions, respectively. Finally, the system uses the intent and entity information to make a query in the knowledge graph and provides users with appropriate answers, thus completing the construction of a medical question-answering system