Construction of Medical Health Knowledge Map for UGC in Online Health Community:Taking Child Diarrheal Disease as an Example
It is of great significance to construct the medical health knowledge map oriented to the user generated content(UGC)data of online health community and explore the health knowledge extraction based on the potential needs of users to optimize the information organization and retrieval of online health community and support the knowledge service innovation of online health community.This paper proposes a combined entity recognition model LDA-BERT-BiLSTM-CRF based on UGC data of online health communities.We use the LDA topic model to perform thematic cluster analysis on UGC data of online health communities to extract entity types.Based on subdivision entity type,BERT-BiLSTM-CRF model is used to identify named entity.Then,MC-BERT-CasRel model is used to extract overlapping triples from UGC data in online health communities.Entity alignment is realized by SBERT model.Finally,the storage and visualization of knowledge map are realized by using Neo4j graph database.Taking child diarrheal disease as an example,a knowledge map of child diarrheal disease containing 939 entities and 3 224 relationships is constructed based on this method.Compared with the current mainstream models,the results show that the combined model LDA-BERT-BiLSTM-CRF and the relationship extraction model MC-BERT-CasRel are more accurate than the traditional knowledge extraction methods,and the entity classification is more targeted.
Knowledge Map ConstructionOnline Health CommunityUGCLDAKnowledge Extraction
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