Knowledge Graph Representation Learning

Knowledge Graph Representation Learning is a computational framework that maps entities and relations within a knowledge graph to low-dimensional vector spaces while preserving semantic structures and enabling relational reasoning through learned embeddings.

1.1K

Publications

86.8K

Citations

3.4K

Authors

714

Institutions

Publications per year

2017–2026

1K

Variants

Kgrl

Authors

3.4K

  1. ZL
    Zhiyuan Liu

    Tsinghua University, China

    25 publications · 7.8K citations

    20

    h-index

  2. HC
    Huajun Chen

    Zhejiang University, China

    25 publications · 1.6K citations

    18

    h-index

  3. JL
    Juanzi Li

    Tsinghua University, China

    14 publications · 2.1K citations

    14

    h-index

  4. VT
    Volker Tresp

    Siemens (Germany), Germany

    16 publications · 745 citations

    14

    h-index

  5. MS
    Maosong Sun

    Tsinghua University, China

    14 publications · 6.3K citations

    13

    h-index

1–5 of 3.4K

Venues

  1. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 69 publications · 14.5K citations

    45

    h-index

  2. arXiv (Cornell University)

    Repository · 148 publications · 8.6K citations

    41

    h-index

  3. Knowledge-Based Systems

    49 publications · 2K citations

    28

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 22 publications · 4K citations

    20

    h-index

  5. Expert Systems with Applications

    26 publications · 1.1K citations

    20

    h-index

1–5