Knowledge Graph Mining

Knowledge Graph Mining is a computational methodology within graph mining that specifically focuses on extracting, inferring, and enriching structured knowledge from knowledge graphs by leveraging their semantic and relational characteristics to discover novel, meaningful patterns or facts.

204

Publications

10K

Citations

709

Authors

290

Institutions

Publications per year

2017–2026

166

Authors

709

  1. HP
    Heiko Paulheim

    University of Mannheim, Germany

    4 publications · 1.3K citations

    4

    h-index

  2. JP
    Jay Pujara

    University of Southern California, United States

    3 publications · 58 citations

    3

    h-index

  3. XL
    Xin Luna Dong

    Google (United States), United States

    3 publications · 103 citations

    3

    h-index

  4. JD
    Jens Dörpinghaus

    Fraunhofer Institute for Algorithms and Scientific Computing, Germany

    3 publications · 45 citations

    3

    h-index

  5. YW
    Yinghui Wu

    Washington State University, United States

    3 publications · 136 citations

    3

    h-index

1–5 of 709

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 7 publications · 662 citations

    7

    h-index

  2. arXiv (Cornell University)

    Repository · 6 publications · 123 citations

    6

    h-index

  3. Expert Systems with Applications

    5 publications · 190 citations

    5

    h-index

  4. Knowledge-Based Systems

    4 publications · 252 citations

    5

    h-index

  5. IEEE Access

    4 publications · 187 citations

    4

    h-index

1–5