Medical Knowledge Graph Question-Answering System Based on Hybrid Dynamic Masking and Multi-strategy Fusion

Abstract

Medical knowledge graph question-answering combines medical knowledge and natural language processing technology to provide accurate and fast question-answering services for medical practitioners and patients. However, the current Chinese medical knowledge graphs are not comprehensive enough due to the surge in data. Additionally, the complex and ambiguous nature of medical questions poses a significant challenge in accurately identifying entity information and generating answers that are both easily comprehensible and accessible to the public. This paper proposes a medical knowledge graph question-answering framework based on hybrid dynamic masking and multi-strategy fusion. Initially, a medical knowledge graph encompassing 34167 entities and 297463 relationships is constructed by integrating public datasets and disease knowledge from medical platforms, covering categories such as diseases, medications, and food. Subsequently, a BERT-MaskAttention-BiLSTM-CRF hybrid dynamic masking model is introduced to accurately identify medical entity information in the input, effectively focusing on essential content and eliminating interference from redundant information. Finally, entity alignment strategies are employed to unify and standardize medical entities, while intent recognition strategies delve into users’ query intentions. This is coupled with the use of large language models to refine the output from the knowledge graph, ensuring that the responses are more readily comprehensible. Experimental results demonstrate that the model achieves a macro-average F1 score of 0.9602 in entity recognition comparative experiments and an average accuracy of 0.9656 in question-answering tests. The generated content is more easily comprehensible and interpretable.

Keywords

• hybrid dynamic masking; multi-strategy fusion; knowledge graph; medical question-answering; large language model