摘要：

Abstract:

【Purpose/significance】Research on fusing the medical knowledge in knowledge graph to analyze the named entities and re⁃

lationships in user questions and to improve the semantic parsing ability of the intelligent medical question answering system, so as to

provide users with more effective self-help medical question answering services.【Method/process】Medical knowledge is firstly col⁃

lected to build medical knowledge graph, then the knowledge in the graph is used to strengthen the named-entity recognition model

based on multi-attention mechanism to analyze the entities in medical questions, and the relationship extraction model based on the

BERT-BiLSTM to extract relations, finally, the analysis results are used to generate query statements and obtain answers from the

knowledge graph.

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Result/conclusion】Through comparative experiments, the semantic parsing model designed in this paper is com⁃

pared with other models commonly used in intelligent question answering on the medical question datasets, and it is found that the ac⁃

curacy rate, recall rate and F1 score are improved, which verifies that the intelligent question answering system in this paper can un⁃

derstand user questions more accurately.【Innovation/limitation】This paper constructs a medical knowledge graph and a question an⁃

swering system combining the graph knowledge and the semantic parsing model based on multi-attention mechanism, which has better

performance in question answering tasks, and the parsing of complex questions needs further research.