Text-to-concept Mapping

Text-to-Concept Mapping is a representational process in cognitive science and artificial intelligence that identifies and establishes correspondences between textual elements and underlying conceptual structures, distinguishing itself by transforming unstructured natural language descriptions into formalized, machine-processable knowledge representations.

169

Publications

11.2K

Citations

404

Authors

170

Institutions

Publications per year

2017–2026

24

Authors

404

  1. PL
    Peilin Liu

    Shanghai Jiao Tong University, China

    3 publications · 404 citations

    3

    h-index

  2. AR
    Alexander Renkl

    University of Freiburg, Germany

    3 publications · 253 citations

    3

    h-index

  3. JV
    Jorge Villalón

    The University of Sydney, Australia

    2 publications · 145 citations

    2

    h-index

  4. SC
    Shang-Cheng Chiu Huang

    2 publications · 43 citations

    2

    h-index

  5. TS
    Tatjana S. Hilbert

    University of Freiburg, Germany

    2 publications · 228 citations

    2

    h-index

1–5 of 404

Venues

  1. Computers & Education

    7 publications · 856 citations

    7

    h-index

  2. Expert Systems with Applications

    5 publications · 239 citations

    5

    h-index

  3. Cognition

    3 publications · 942 citations

    3

    h-index

  4. Data & Knowledge Engineering

    DKE · 2 publications · 37 citations

    2

    h-index

  5. PubMed

    Repository · 2 publications · 158 citations

    2

    h-index

1–5