摘要
[目的/意义]实体关系联合抽取是药物不良反应监测和知识组织的关键环节。为解决传统流水线抽取方法中误差传递、实体冗余和交互缺失问题,提升药物不良反应重叠三元组抽取效果,提出了一种基于异构图注意力网络的药物不良反应实体关系联合抽取模型MF-HGAT。[方法/过程]首先通过BERT预训练进行外部医学语料资源的知识迁移,实现多语义特征融合;其次将关系信息作为先验知识引入为异构图节点,以避免提取语义无关实体;然后通过迭代融合异构图注意力网络消息传递机制增强字符与关系节点表示;最后在节点表示更新后抽取药物不良反应实体关系。[结果/结论]在自构建药物不良反应数据集上进行实验,发现融入关系信息和外部医疗健康领域知识的MF-HGAT联合抽取F1值达到了92.75%,较主流模型CasRel提升了5.29%。研究结果表明,MF-HGAT模型通过异构图注意力网络融合字符与关系节点语义,可有效解决药物不良反应实体关系重叠问题,对药物不良反应知识发现具有重要意义。
[Purpose/Significance]Joint extraction of entities and relations is a crucial component in the adverse drug reactions monitoring and knowledge organization.To address the issues of error propagation,entity redundancy and interac-tion deficiency in traditional pipeline extraction methods,and to improve the extraction effect of overlapping ternary groups of adverse drug reactions,the paper proposes a joint extraction model of adverse drug reactions entities and relations based on heterogeneous graph attention network MF-HGAT.[Method/Process]Firstly,the paper conducted knowledge trans-fered from external medical corpus resources through pre-training with BERT to achieve the fusion of multiple semantic fea-tures.Secondly,the paper introduced relations information as prior knowledge for heterogeneous graph nodes to avoid ex-tracting semantically irrelevant entities.Then,the paper enhanced the representations of characters and relations nodes by iteratively fusing messages with a hierarchical graph attention network through message passing.Finally,the paper extracted drug adverse reactions entities and relations after updating the node representations.[Result/Conclusion]Experiments on self-constructed adverse drug reactions datasets reveal that the joint extraction F1 value of MF-HGAT,which incorporates relations information and external medical and health domain knowledge,reaches 92.75%,which is an improvement of 5.29%over the mainstream model CasRel.The results demonstrate that the MF-HGAT model further enriches entity-rela-tions semantic information by fusing character and relations node semantics through heterogeneous graph attention network,which is of great significance to the knowledge discovery of adverse drug reactions.
作者
仲雨乐
韩普
许鑫
Zhong Yule;Han Pu;Xu Xin(School of Economics and Management,East China Normal University,Shanghai 200062,China;School of Management,Nanjing University of Posts&Telecommunications,Nanjing 210003,China;Jiangsu Provincial Key Laboratory of Data Engineering and Knowledge Service,Nanjing 210023,China)
出处
《现代情报》
CSSCI
北大核心
2024年第9期71-81,共11页
Journal of Modern Information
基金
国家社会科学基金项目“面向多模态医疗健康数据的知识组织模式研究”(项目编号:22BTQ096)
江苏高校青蓝工程
南京邮电大学1311人才计划
关键词
异构图注意力网络
实体关系联合抽取
药物不良反应
关系重叠
知识发现
heterogeneous graph attention network
joint entity relation extraction
adverse drug reactions
relations overlap
knowledge discovery
