Intelligent Practice Exploration of Large Language Model in Medical Emergency Knowledge Graph Question-and-Answer Service
At present,the medical emergency response plan field in China is large in scale but not highly intelligent.The knowledge graph question answering system can transfer the operation core from humans to machines,thus achieving intelligence.However,the high-quality knowledge graph scale in this field is relatively small,and the question answering matching task has problems of inefficient retrieval and com-plex processing flow.The big language model provides a new direction for knowledge graph question answering.Explore the integration of open-source big models with intelligent medical emergency systems,construct a knowledge graph in the vertical field of medical emergency,and propose a method for enhancing knowledge graph Q&A with open-source big models.This method involves post production retrieval,uti-lizing fine tuned open-source models to generate query statements.A dictionary composed of knowledge graph entities and relationships replac-es the generated query statements with entities and relationships,and obtains knowledge graph answers through standardized query statements.After experimental testing,the logical accuracy of this method on the test set reached 84.16%,which is feasible on self built knowledge graphs and has reference value for other fields.
large language modelknowledge graph Q&Aintelligenceemergency plan
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