Abstract: As a powerful tool for integrating massive medical information,medical knowledge graphs are being widely evaluated on convenient platforms such as clinical decision support systems and medical question and answer systems.At present,large-scale medical knowledge graphs are emerging one after another,but most of them focus on the supplement of the number of entities.Medical terminology is lengthy and difficult to understand.Therefore,building a fine-grained knowledge graph can make the knowledge graph convenient for the system to a large extent.practicality and provide more crown diagnostic instructions for the question and answer system.This paper targets the large-scale medical knowledge base crawled by vertical websites,with the goal of achieving fine-grained medical long texts.BiLSTM is used to model complete contextual information for each word from both directions of the long sentence.At the same time,we introduce the pre-training model BERT to enhance the modeling of word context semantics and combined with the CRF model learning status.The incremental matrix maintains the consistency of the label sequence,efficiently identifies entities in long sentences,and builds a fine-grained medical knowledge graph through entity alignment and attribute filling.Comparative experiments on the fine-grained task of medical entities demonstrate that the BERT+BiLSTM+CRF model is better than other models,and the visualization results also illustrate the fine-grained effect of this method.

Key words: Knowledge graph, BiLSTM, CRF, Fine-grained