Automatic Question-Answering in Chinese Medical Q & A Community with Knowledge Graph

[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. © 2022 Food and Fermentation Industries. All rights reserved.