Automatic Question-Answering in Chinese Medical Q & A Community with Knowledge Graph
Wang Yinqiu1,2,Yu Wei1,2(),Chen Junpeng3
1Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China 2School of Information Management, Nanjing University, Nanjing 210023, China 3College of Information Engineering, Nanjing University of Finance & Economics, Nanjing 210023, China
[Objective] This paper proposes a new method to determine the reliability of answers from the online Chinese medical question and answer (Q&A) community, aiming to enhance the accuracy of answer selection models for medical Q&A recognition with the help of professional medical knowledge graphs. [Methods] Based on the answer selection model using a hybrid neural network (fusing RNN and multi-scale CNN to capture context and local information), we constructed a professional medical knowledge graph that integrated entity and relationship embeddings to enrich the semantic information of the Q&A text. Combined with the Q&A pair attention mechanism, we obtained the final similarity of the pairs and selected candidate answers with the highest scores. [Results] We examined the proposed model on the cMedQA2.0 dataset. Compared to the hybrid neural network model without incorporating knowledge graph entity relationship, the Top-1 accuracy of the answer selection in our new model increased by 2.3% (to 62.2%), demonstrating its effectiveness for improving answer selection. [Limitations] The medical knowledge graph used is of small size, only including the common entities in the medical community Q&A. The incomplete relationship between medical entities may affect the answer selection effectiveness when facing niche questions. [Conclusions] Combining professional Chinese medical knowledge graphs and deep learning models could improve the answer selection technology. It helps people with medical consultation needs obtain reliable medical advice in the Q & A community. Our model also monitors the online medical community’s information quality and reduces the burden of hospital outpatient service.
王寅秋, 虞为, 陈俊鹏. 融合知识图谱的中文医疗问答社区自动问答研究*[J]. 数据分析与知识发现, 2023, 7(3): 97-109.
Wang Yinqiu, Yu Wei, Chen Junpeng. Automatic Question-Answering in Chinese Medical Q & A Community with Knowledge Graph. Data Analysis and Knowledge Discovery, 2023, 7(3): 97-109.
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