A Unified and Evolvable Knowledge Graph Management Mechanism for Medical Data

Tracking #: 3126-4340

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Linlin You
Gengxiang Chen
Hongli Li
Xuan Jiang
Yuren Zhou

Responsible editor: 
Guest Editors SW Meets Health Data Management 2022

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Full Paper
To ease the management of medical data changing over time, knowledge graphs (KGs) have been widely utilized in life science and healthcare as a common approach to preserving domain-related knowledge. However, the dynamic feature of data can lead to KGs in different versions, which manage the same knowledge but with some of the semantic triples altered. To reduce the overwhelming knowledge duplication of versioned KGs to save storage spaces, and analyze the evolution correlations hidden behind these versions to accelerate the information query process, an appropriate method to integrate the knowledge of all the versions is of great significance. Therefore, in this study, a unified and evolvable knowledge graph management mechanism (EKG2M) is proposed, in which evolution records among versions can be first computed and then merged into an Evolvable Knowledge Graph (EKG) designed according to a unified evolution data structure. After the merging, the generated EKG can be used to support not only conventional operations, i.e., searching for entities or relationships, but also novel queries, i.e., revealing entity evolution routes. Moreover, the assessment of EKG2M shows that it is able to detect the evolution record accurately and reduce both storage space and query time efficiently and effectively.
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