A Knowledge Graph Construction for Q&A Text in Chinese Online Medical Community
[Purpose/Significance]This paper designs a set of knowledge graph construction method with some deep learning methods to facilitate knowledge extraction from colloquial,noisy and poorly normalized on-line medical community Q&A texts.[Method/Process]This paper utilized diabetes-related Q&A texts from xywy.com as the dataset,and determined entity and relationship categories through an analysis of the healthcare needs of the community users.The BERT-wwm model was employed for word embedding to solve polysemy,and then the BiLSTM-CRF model for entity recognition.When annotating the relations between entities,an entity mask was de-signed to avoid the relation overlap,and the CNN-Attention model was adopted for relation extraction.Ultimately,structured data was obtained through entity alignment using dictionary matching and entity name similarity,and stored and visualized using Neo4j.[Result/Conclusion]Experiments verify the effectiveness of the above methods.This paper extracts the medical knowledge from non-structured OMC text into structured data,which can promote the community knowledge discovery and online intelligent health services.
online medical communityknowledge graphBERTattention mechanismdeep learning
万方数据
维普
