Chinese Journal of Intelligent Science and Technology ›› 2022, Vol. 4 ›› Issue (1): 55-64.doi: 10.11959/j.issn.2096-6652.202217

Special Issue: 知识图谱

• Special Topic: Crowd Intelligence • Previous Articles     Next Articles

Collective knowledge graph: meta knowledge transfer and federated graph reasoning

Mingyang CHEN1, Wen ZHANG2, Xiangnan CHEN2, Hongting ZHOU1, Huajun CHEN1   

  1. 1 College of Computer Science and Technology, Zhejiang University, Hangzhou 310007, China
    2 School of Software Technology, Zhejiang University, Hangzhou 310007, China
  • Revised:2022-02-25 Online:2022-03-15 Published:2022-03-01
  • Supported by:
    The National Natural Science Foundation of China(91846204);The National Natural Science Foundation of China(U19B2027)


Collective knowledge graphs refer to knowledge graphs that are managed and maintained in a decentralized or distributed manner through group collaboration.Compared with the existing centrally managed knowledge graph, the collective knowledge graph has the characteristics of knowledge right confirmation, privacy protection, crowd sourcing incentive, and credible traceability.Tring to explore the technical challenges faced by building and applying a collective knowledge graph platform.For meta knowledge transfer, the knowledge incompleteness of a single knowledge graph by knowledge transfer among multiple knowledge graphs from different sources under a decentralized and autonomous framework was considered.The main difficulty was to enhance the respective knowledge graph representation by sharing useful knowledge with each other as much as possible while fully protecting the autonomous ownership of knowledge.For federated graph reasoning, the knowledge graph reasoning in a distributed environment under the privacy-preserving by means of the federated learning mechanism was considered.Meta knowledge transfer focused on transferring entity-independent knowledge between knowledge graphs with overlapped relation set, while federated graph reasoning aimed at learning better entity embeddings for knowledge graphs with overlapped entity set.The model design and experimental validation for each of these two problems were conducted.

Key words: collective knowledge graph, federated learning, knowledge transfer

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