Feature Engineering For Knowledge Graphs

Feature Engineering for Knowledge Graphs is a methodological framework within domain-specific feature design that systematically constructs and transforms structural, semantic, and relational attributes from knowledge graph data to enhance the performance of machine learning models in knowledge-driven tasks.

20

Publications

768

Citations

79

Authors

40

Institutions

Publications per year

2017–2026

13

Authors

79

  1. EB
    Elisa Bertino

    Purdue University West Lafayette, United States

    1 publications · 78 citations

    1

    h-index

  2. JC
    Jiaoyan Chen

    University of Oxford, United Kingdom

    1 publications · 19 citations

    1

    h-index

  3. NZ
    Ningyu Zhang

    Zhejiang University, China

    1 publications · 19 citations

    1

    h-index

  4. MA
    Martin Atzmueller

    University of Kassel, Germany

    1 publications · 12 citations

    1

    h-index

  5. JV
    Jan Vanthienen

    KU Leuven, Belgium

    1 publications · 10 citations

    1

    h-index

1–5 of 79

Venues

  1. IEEE Internet of Things Journal

    1 publications · 23 citations

    1

    h-index

  2. IEEE Access

    1 publications · 29 citations

    1

    h-index

  3. Knowledge-Based Systems

    1 publications · 39 citations

    1

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 28 citations

    1

    h-index

  5. Bioinformatics

    1 publications · 151 citations

    1

    h-index

1–5