Automated Concept Mapping

Automated Concept Mapping is a computational method that generates concept maps from textual or discourse data without manual intervention, distinguishing itself from manual or semi-automated approaches by fully leveraging algorithms to identify, extract, and organize key concepts and their interrelationships.

101

Publications

4.7K

Citations

290

Authors

125

Institutions

Publications per year

2017–2026

19

Authors

290

  1. SC
    Shyi‐Ming Chen

    National Taiwan University of Science and Technology, Taiwan

    5 publications · 200 citations

    5

    h-index

  2. PT
    Paolo Tonella

    Fondazione Bruno Kessler, Italy

    3 publications · 98 citations

    3

    h-index

  3. RA
    Rafael A. Calvo

    The University of Sydney, Australia

    2 publications · 98 citations

    2

    h-index

  4. KŽ
    Krunoslav Žubrinić

    University of Dubrovnik, Croatia

    2 publications · 62 citations

    2

    h-index

  5. YG
    Yann‐Gaël Guéhéneuc

    Polytechnique Montréal, Canada

    2 publications · 21 citations

    2

    h-index

1–5 of 290

Venues

  1. Expert Systems with Applications

    8 publications · 421 citations

    8

    h-index

  2. PubMed

    Repository · 3 publications · 74 citations

    3

    h-index

  3. Computers & Education

    3 publications · 299 citations

    3

    h-index

  4. IEEE Transactions on Learning Technologies

    3 publications · 130 citations

    3

    h-index

  5. Computer Science Education

    2 publications · 66 citations

    2

    h-index

1–5