metagol/metagol: papers linking to it

GitHub · 212 stars · 28 forks · Prolog · license BSD-3-Clause · created 2015 · archived · github.com/metagol/metagol

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to metagol/metagol (https://github.com/metagol/metagol), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

Key findings#

14 papers in the tracked journals link to the GitHub repository metagol/metagol in their Crossref records, the first in 2018; most of them (3) in 2023.
By category: AI (14); the journals with the most are Machine Learning (14).
13 of the papers build on one another: a later paper cites an earlier one that also links metagol/metagol.

By year#

20182025

Papers linking to metagol/metagol by publication year
Year	Papers
2018	1
2019	3
2021	3
2022	2
2023	3
2024	1
2025	1

Journals#

Journals whose papers link to metagol/metagol most
Journal	Papers
Machine Learning	14

Family tree#

Each paper sits under the earlier paper it builds on; papers that cite no other paper here start a branch of their own.

Learning efficient logic programs Machine Learning, 2018
- Inductive general game playing Machine Learning, 2019 — cites the paper above
  - Logical reduction of metarules Machine Learning, 2019 — cites the paper above
    - Learning programs by learning from failures Machine Learning, 2021 — cites the paper above
      - Meta-interpretive learning as metarule specialisation Machine Learning, 2022 — cites the paper above
      - Learning logic programs by explaining their failures Machine Learning, 2023 — cites the paper above
- Learning higher-order logic programs Machine Learning, 2019 — cites the paper above
- Beneficial and harmful explanatory machine learning Machine Learning, 2021 — cites the paper above
  - Inductive logic programming at 30 Machine Learning, 2021 — cites the paper above
    - $$\alpha$$ILP: thinking visual scenes as differentiable logic programs Machine Learning, 2023 — cites the paper above
      - Learning answer set programs with aggregates via sampling and genetic programming Machine Learning, 2025 — cites the paper above
    - Rule learning by modularity Machine Learning, 2024 — cites the paper above
  - Explanatory machine learning for sequential human teaching Machine Learning, 2023 — cites the paper above

Papers linking to metagol/metagol, most cited first
Published	Title	Journal	Authors	Citations	Link
2021-02-19	Learning programs by learning from failures	Machine Learning	Andrew Cropper, Rolf Morel	86	cites it
2021-11-09	Inductive logic programming at 30	Machine Learning	Andrew Cropper, Sebastijan Dumančić, Richard Evans et al.	61	cites it
2021-03-11	Beneficial and harmful explanatory machine learning	Machine Learning	Lun Ai, Stephen Muggleton, Céline Hocquette et al.	34	cites it
2018-04-23	Learning efficient logic programs	Machine Learning	Andrew Cropper, Stephen Muggleton	33	cites it
2019-11-20	Logical reduction of metarules	Machine Learning	Andrew Cropper, Sophie Tourret	29	cites it
2019-11-18	Inductive general game playing	Machine Learning	Andrew Cropper, Richard Evans, Mark Law	26	cites it
2023-03-14	$$\alpha$$ILP: thinking visual scenes as differentiable logic programs	Machine Learning	Hikaru Shindo, Viktor Pfanschilling, Devendra Singh Dhami et al.	21	cites it
2019-12-03	Learning higher-order logic programs	Machine Learning	Andrew Cropper, Rolf Morel, Stephen Muggleton	11	cites it
2023-06-28	Explanatory machine learning for sequential human teaching	Machine Learning	Lun Ai, Johannes Langer, Stephen Muggleton et al.	6	cites it
2023-08-07	Learning logic programs by explaining their failures	Machine Learning	Rolf Morel, Andrew Cropper	4	cites it
2022-04-01	Lifting symmetry breaking constraints with inductive logic programming	Machine Learning	Alice Tarzariol, Martin Gebser, Konstantin Schekotihin	3	cites it
2022-04-15	Meta-interpretive learning as metarule specialisation	Machine Learning	Stassa Patsantzis, Stephen Muggleton	3	cites it
2025-05-20	Learning answer set programs with aggregates via sampling and genetic programming	Machine Learning	Damiano Azzolini	3	cites it
2024-07-03	Rule learning by modularity	Machine Learning	Albert Nössig, Tobias Hell, Georg Moser	2	cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). metagol/metagol: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-metagol-metagol

@misc{scholar4ai-code-github-metagol-metagol,
  author = {{Scholar4AI}},
  title = {metagol/metagol: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-metagol-metagol}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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