Abstract
Knowledge graphs can support different types of services and are a valuable source. Automatic methods have been widely used in many domains to construct the knowledge graphs. However, it is more complex and difficult in the medical domain. There are three reasons: (1) the complex and obscure nature of medical concepts and relations, (2) inconsistent standards and (3) heterogeneous multi-source medical data with low quality like EMRs (Electronic Medical Records). Therefore, the quality of knowledge requires a lot of manual efforts from experts in the process. In this paper, we introduce an overall framework called DocKG that provides insights on where and when to import manual efforts in the process to construct a health knowledge graph. In DocKG, four tools are provided to facilitate the doctors’ contribution, i.e. matching synonym, discovering and editing new concepts, annotating concepts and relations, together with establishing rule base. The application for cardiovascular diseases demonstrates that DocKG could improve the accuracy and efficiency of medical knowledge graph construction.
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Acknowledgments
This work was supported by NSFC (91646202), National Key R&D Program of China (2018YFB1404400, 2018YFB1402700).
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Sheng, M. et al. (2019). DocKG: A Knowledge Graph Framework for Health with Doctor-in-the-Loop. In: Wang, H., Siuly, S., Zhou, R., Martin-Sanchez, F., Zhang, Y., Huang, Z. (eds) Health Information Science. HIS 2019. Lecture Notes in Computer Science(), vol 11837. Springer, Cham. https://doi.org/10.1007/978-3-030-32962-4_1
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DOI: https://doi.org/10.1007/978-3-030-32962-4_1
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