Linked Data Curation

Linked Data Curation is a scholarly method within the domain of knowledge curation that systematically organizes, maintains, and enhances interconnected datasets on the web by applying semantic web standards to ensure interoperability, accuracy, and long-term accessibility of linked data.

56

Publications

2.7K

Citations

192

Authors

66

Institutions

Publications per year

2017–2026

7

Authors

192

  1. SA
    Sören Auer

    University of Bonn, Germany

    5 publications · 1K citations

    5

    h-index

  2. DL
    Deborah L. McGuinness

    Rensselaer Polytechnic Institute, United States

    3 publications · 303 citations

    3

    h-index

  3. AG
    Alvaro Graves

    Rensselaer Polytechnic Institute, United States

    3 publications · 303 citations

    3

    h-index

  4. JL
    Jens Lehmann

    University of Bonn, Germany

    3 publications · 1K citations

    3

    h-index

  5. LD
    Li Ding

    University of Maryland, Baltimore County, United States

    3 publications · 303 citations

    3

    h-index

1–5 of 192

Venues

  1. Semantic Web

    6 publications · 701 citations

    6

    h-index

  2. Journal of Web Semantics

    2 publications · 152 citations

    2

    h-index

  3. Expert Systems with Applications

    2 publications · 39 citations

    2

    h-index

  4. Computers and Electronics in Agriculture

    1 publications · 43 citations

    1

    h-index

  5. Sports Health A Multidisciplinary Approach

    SPH · 1 publications · 54 citations

    1

    h-index

1–5