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个人信息Personal Information
教授
博士生导师
硕士生导师
性别:女
毕业院校:大连理工大学
学位:博士
所在单位:计算机科学与技术学院
学科:计算机应用技术
办公地点:创新园大厦B811
联系方式:0411-84706009-2811
电子邮箱:wangjian@dlut.edu.cn
A Knowledge Graph based Bidirectional Recurrent Neural Network Method for Literature-based Discovery
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论文类型:会议论文
发表时间:2018-01-01
收录刊物:CPCI-S
页面范围:751-752
关键字:literature-based discovery; knowledge graph; bidirectional recurrent neural network; drug discovery
摘要:In this paper, we present a model which incorporates biomedical knowledge graph, graph embedding and deep learning methods for literature-based discovery. Firstly, the relations between entities are extracted from biomedical abstracts and then a knowledge graph is constructed by using these obtained relations. Secondly, the graph embedding technologies are applied to convert the entities and relations in the knowledge graph into a low-dimensional vector space. Thirdly, a bidirectional Long Short-Term Memory network is trained based on the entity associations represented by the pre-trained graph embeddings. Finally, the learned model is used for open and closed literature-based discovery tasks. The experimental results show that our method could not only effectively discover hidden associations between entities, but also reveal the corresponding mechanism of interactions. It suggests that incorporating knowledge graph and deep learning methods is an effective way for capturing the underlying complex associations between entities hidden in the literature.