DEKGB: An Extensible Framework for Health Knowledge Graph

  • Ming Sheng
  • Yuyao Shao
  • Yong ZhangEmail author
  • Chao Li
  • Chunxiao Xing
  • Han Zhang
  • Jingwen Wang
  • Fei Gao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11924)


With the progress of medical informatization and the substantial growth of clinical data, knowledge graph is playing an increasingly important role in medical domain. Medical domain is highly specialized with abundant high-quality ontologies, and has many professional sub-fields such as cardiovascular diseases, diabetes mellitus and so on. It is very difficult to build a health knowledge graph for all of the diseases because of data availability and deep involvement of doctors. In this paper, we propose an efficient and extensible framework, DEKGB, to construct knowledge graphs for specific diseases based on prior medical knowledge and EMRs with doctor-involved. After that, we present the detailed process how DEKGB is applied to extend an existing health knowledge graph to include a new disease. It is confirmed that using this framework, doctors can get highly specialized health knowledge graphs conveniently and efficiently.


Health knowledge graph Extensibility Conceptual graph Instance graph 



This work was supported by NSFC (91646202), National Key R&D Program of China (2018YFB1404400, 2018YFB1402700).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ming Sheng
    • 1
  • Yuyao Shao
    • 2
  • Yong Zhang
    • 1
    Email author
  • Chao Li
    • 1
  • Chunxiao Xing
    • 1
  • Han Zhang
    • 3
  • Jingwen Wang
    • 2
  • Fei Gao
    • 4
  1. 1.RIIT&BNRCIST&DCST, Tsinghua UniversityBeijingChina
  2. 2.Beijing Foreign Studies UniversityBeijingChina
  3. 3.Beijing University of Posts and TelecommunicationsBeijingChina
  4. 4.Henan Justice Police Vocational CollegeZhengzhouChina

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