Research on Automatic Question Answering Systems Based on Medical Knowledge Graphs

In recent years, intelligent question answering systems have played an increasingly significant role in the healthcare domain. However, traditional retrieval-based and rule-based methods struggle to cope with the complexity and diversity of medical knowledge, resulting in significant shortcomings in the accuracy and reasoning capabilities of question answering systems. To address this issue, this paper proposes an automatic question answering system based on a medical knowledge graph. Firstly, a large-scale medical knowledge graph is constructed to represent medical entities (such as diseases, drugs, and symptoms) and their relationships. Secondly, a question answering model combining graph reasoning and natural language processing (NLP) is designed. Through modules such as entity recognition, relation extraction, and graph reasoning, semantic understanding and reasoning of complex medical questions are realized. Experimental results demonstrate that the proposed system achieves significant improvements in question answering accuracy and semantic reasoning ability compared to traditional methods, and can effectively answer diverse questions in the medical domain. This research provides a new perspective for the design and implementation of medical question answering systems and has high practical value.