Knowledge Graph Fusion

Knowledge Graph Fusion is a computational framework that integrates heterogeneous knowledge graphs by aligning and reconciling overlapping entities, relations, and schemas to produce a unified, coherent knowledge graph while preserving and enhancing semantic meaning and structural integrity.

65

Publications

2.9K

Citations

273

Authors

94

Institutions

Publications per year

2017–2026

63

Authors

273

  1. JD
    Jingtao Ding

    Tsinghua University, China

    2 publications · 72 citations

    2

    h-index

  2. YQ
    Yanru Qu

    Shanghai Jiao Tong University, China

    2 publications · 223 citations

    2

    h-index

  3. YL
    Yu Liu

    Tsinghua University, China

    2 publications · 72 citations

    2

    h-index

  4. WZ
    Wen Zhang

    Zhejiang University, China

    2 publications · 44 citations

    2

    h-index

  5. YH
    Yufeng Huang

    Zhejiang University, China

    2 publications · 44 citations

    2

    h-index

1–5 of 273

Venues

  1. Applied Intelligence

    4 publications · 71 citations

    4

    h-index

  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 3 publications · 114 citations

    3

    h-index

  3. arXiv (Cornell University)

    Repository · 3 publications · 76 citations

    3

    h-index

  4. Information Fusion

    3 publications · 227 citations

    3

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 3 publications · 172 citations

    3

    h-index

1–5