Automated Query Generation

Automated Query Generation is a computational method that systematically produces database or information retrieval queries from structured or unstructured inputs without requiring manual formulation by users.

364

Publications

18.1K

Citations

1K

Authors

332

Institutions

Publications per year

2017–2026

157

Variants

Aqg

Authors

1K

  1. CA
    Craig A. Knoblock

    University of Southern California, United States

    5 publications · 414 citations

    5

    h-index

  2. BC
    Bikash Chandra

    Indian Institute of Technology Bombay, India

    4 publications · 112 citations

    4

    h-index

  3. SS
    S. Sudarshan

    Indian Institute of Technology Bombay, India

    4 publications · 112 citations

    4

    h-index

  4. DR
    Dragomir Radev

    University of Michigan, United States

    4 publications · 389 citations

    4

    h-index

  5. DG
    Dániel Gerber

    Lawrence Berkeley National Laboratory, United States

    3 publications · 155 citations

    4

    h-index

1–5 of 1K

Venues

  1. arXiv (Cornell University)

    Repository · 23 publications · 1.7K citations

    17

    h-index

  2. Proceedings of the VLDB Endowment

    PVLDB · 22 publications · 1.2K citations

    17

    h-index

  3. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 8 publications · 276 citations

    10

    h-index

  4. Information Processing & Management

    4 publications · 162 citations

    6

    h-index

  5. Journal of Intelligent Information Systems

    4 publications · 86 citations

    4

    h-index

1–5