Knowledge Graph Reasoning

Knowledge Graph Reasoning is a computational method that derives implicit, semantically consistent knowledge from explicit facts within a knowledge graph by leveraging logical, symbolic, or neural inference mechanisms to infer novel relationships, resolve inconsistencies, or complete missing information.

555

Publications

30.4K

Citations

2.1K

Authors

540

Institutions

Publications per year

2017–2026

531

Variants

Knowledge Graph Inference

Authors

2.1K

  1. HC
    Huajun Chen

    Zhejiang University, China

    9 publications · 591 citations

    9

    h-index

  2. JL
    Jure Leskovec

    Stanford University, United States

    8 publications · 376 citations

    8

    h-index

  3. JC
    Jiaoyan Chen

    University of Oxford, United Kingdom

    7 publications · 317 citations

    7

    h-index

  4. SP
    Shirui Pan

    Monash University, Australia

    6 publications · 206 citations

    6

    h-index

  5. KP
    Karuna Pande Joshi

    University of Maryland, Baltimore County, United States

    6 publications · 242 citations

    6

    h-index

1–5 of 2.1K

Venues

  1. arXiv (Cornell University)

    Repository · 66 publications · 2.5K citations

    28

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 19 publications · 2.2K citations

    17

    h-index

  3. Knowledge-Based Systems

    21 publications · 888 citations

    17

    h-index

  4. Neurocomputing

    14 publications · 335 citations

    13

    h-index

  5. Expert Systems with Applications

    10 publications · 1.4K citations

    10

    h-index

1–5