ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2017, Vol. 54 ›› Issue (12): 2687-2697.doi: 10.7544/issn1000-1239.2017.20170640

所属专题: 2017人工智能应用专题

• 人工智能 • 上一篇    下一篇

一种面向临床领域时序知识图谱的链接预测模型

陈德华1,殷苏娜1,乐嘉锦1,王梅1,潘乔1,朱立峰2   

  1. 1(东华大学计算机科学与技术学院 上海 201600); 2(上海交通大学医学院附属瑞金医院 上海 200025) (chendehua@dhu.edu.cn)
  • 出版日期: 2017-12-01
  • 基金资助: 
    上海市科技创新行动计划项目(15511106900);上海市科技发展基金项目(16JC1400802);上海市信息化发展专项基金项目(XX-XXFZ-01-14-6349)

A Link Prediction Model for Clinical Temporal Knowledge Graph

Chen Dehua1, Yin Suna1, Le Jiajin1, Wang Mei1, Pan Qiao1, Zhu Lifeng2   

  1. 1(College of Computer Science and Technology, Donghua University, Shanghai 201600); 2(Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025)
  • Online: 2017-12-01

摘要: 知识图谱(knowledge graph)链接预测可以解决知识图谱中缺失信息的发现和还原,是目前知识图谱领域的研究热点.传统的知识图谱链接预测方法大多面向静态的数据,并不适用于具有动态变化特性的时序知识图谱.时序知识图谱广泛存在于不同领域中,以临床医学领域为例,糖尿病作为一种典型的慢性病,其病程是一个疾病缓慢发展演化的过程.因此,在临床医学时序知识图谱上进行临床意义的链接预测,比如预测糖尿病的并发症,则需要考虑糖尿病病程发展随时间变化的时序特性,这也为传统的知识图谱链接预测方法带来巨大挑战.为此,结合临床医学事实知识的时序特性,提出一种基于LSTM序列增量学习的临床领域时序知识图谱链接预测模型.该模型结合LSTM长短期记忆单元递归神经网络在序列学习上的优势,通过构建基于LSTM的序列增量学习层,以端到端的方式提取时序知识图谱中的三元组时序特征,从而实现对时序知识图谱的链接预测.通过在糖尿病时序知识图谱上的实验,验证了模型的高效性、可用性及稳定性.

关键词: 时序知识图谱, 知识图谱链接预测, 转换模型TransR, 长短期记忆网络, 增量学习

Abstract: Link prediction on knowledge graph is the main task of knowledge base completion, predicting whether a relationship existing in the knowledge base is likely to be true. However, traditional knowledge link prediction models are only appropriate for static data rather than temporal knowledge base. Temporal knowledge base exists on various fields. Take medical medicine field as example, diabetes is a typical chronic disease which evolves slowly. Thus, link prediction on clinical knowledge base such as diabetic complication requires the analysis on temporal characteristic of temporal knowledge base, which is a great challenge for traditional link prediction models. Thus, to address the prediction of temporal knowledge base, this paper proposes a long short-term memory (LSTM) based model for temporal knowledge base. The proposed model adopts memory cells of LSTM for sequential learning, and then builds incremental learning layer. Afterwards, timing characteristics can be extracted by the way of end-to-end, which realizes the prediction on temporal knowledge base. In experiments, the proposed model in clinical temporal knowledge base shows significant improvements compared with baselines including Rescal, NTN, TransE, TransH, TransR and DNN.

Key words: temporal knowledge graph, knowledge graph link prediction, translation model TransR, long short term memory (LSTM) networks, incremental learning

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