基于深度学习的细粒度医学知识图谱构建  

Construction of Fine-grained Medical Knowledge Graph Based on Deep Learning

作　　者：王钰涵 马涪元 王英[3] WANG Yuhan;MA Fuyuan;WANG Ying(College of Software,Jilin University,Changchun 130012,China;College of Artificial Intelligence,Jilin University,Changchun 130012,China;Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education,Jilin University,Changchun 130012,China)

机构地区：[1]吉林大学软件学院,长春130012 [2]吉林大学人工智能学院,长春130012 [3]符号计算与知识工程教育部重点实验室(吉林大学),长春130012

出　　处：《计算机科学》2024年第S02期26-32,共7页Computer Science

基　　金：国家自然科学基金(62272191);吉林省科技厅重点研发项目(20220201153GX)。

摘　　要：医疗知识图谱作为整合海量医疗信息的有力工具,正被广泛应用于临床决策支持系统、医疗问答系统等便民平台。目前,大规模医疗知识图谱层出不穷,但大多都将注意力放在实体数量的扩充,而忽略了实体种类的细粒度化。医疗术语具有冗长且难以理解的特点,因此构建细粒度化的知识图谱可以在很大程度上提高知识图谱便民系统的实用性,并为问答系统提供更具有针对性的诊断说明。文中针对垂直网站爬取的大规模医疗知识库,以实现医疗长文本细粒度化为目标,运用BiLSTM从长句子的两个方向为每个词语建模完整上下文信息,同时引入预训练模型BERT加强对词语上下文语义的建模,并结合CRF模型学习状态转移矩阵维持标签序列的一致性,高效识别长句中的实体,并通过实体对齐和属性填充构建细粒度医疗知识图谱。医疗实体细粒度化任务的对比实验表明,BERT+BiLSTM+CRF模型的效果优于其他模型,可视化结果也说明了所提方法进行细粒度化的有效性。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.

关 键 词：知识图谱 BiLSTM CRF 细粒度 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]