---
title: "metagol/metagol: papers citing this GitHub repository"
description: "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."
url: https://scholar4ai.com/code/github-metagol-metagol
markdown_url: https://scholar4ai.com/code/github-metagol-metagol.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "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. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# metagol/metagol: papers linking to it

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.

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

All repositories: https://scholar4ai.com/code.md

## 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

*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](https://scholar4ai.com/journals/machine-learning.md) | 14 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Learning efficient logic programs](https://doi.org/10.1007/s10994-018-5712-6) (Machine Learning, 2018)
  - [Inductive general game playing](https://doi.org/10.1007/s10994-019-05843-w) (Machine Learning, 2019) — cites the paper above
    - [Logical reduction of metarules](https://doi.org/10.1007/s10994-019-05834-x) (Machine Learning, 2019) — cites the paper above
      - [Learning programs by learning from failures](https://doi.org/10.1007/s10994-020-05934-z) (Machine Learning, 2021) — cites the paper above
        - [Meta-interpretive learning as metarule specialisation](https://doi.org/10.1007/s10994-022-06156-1) (Machine Learning, 2022) — cites the paper above
        - [Learning logic programs by explaining their failures](https://doi.org/10.1007/s10994-023-06358-1) (Machine Learning, 2023) — cites the paper above
  - [Learning higher-order logic programs](https://doi.org/10.1007/s10994-019-05862-7) (Machine Learning, 2019) — cites the paper above
  - [Beneficial and harmful explanatory machine learning](https://doi.org/10.1007/s10994-020-05941-0) (Machine Learning, 2021) — cites the paper above
    - [Inductive logic programming at 30](https://doi.org/10.1007/s10994-021-06089-1) (Machine Learning, 2021) — cites the paper above
      - [$$\\alpha$$ILP: thinking visual scenes as differentiable logic programs](https://doi.org/10.1007/s10994-023-06320-1) (Machine Learning, 2023) — cites the paper above
        - [Learning answer set programs with aggregates via sampling and genetic programming](https://doi.org/10.1007/s10994-025-06780-7) (Machine Learning, 2025) — cites the paper above
      - [Rule learning by modularity](https://doi.org/10.1007/s10994-024-06556-5) (Machine Learning, 2024) — cites the paper above
    - [Explanatory machine learning for sequential human teaching](https://doi.org/10.1007/s10994-023-06351-8) (Machine Learning, 2023) — cites the paper above

## Papers

*Papers linking to metagol/metagol, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2021-02-19 | [Learning programs by learning from failures](https://doi.org/10.1007/s10994-020-05934-z) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Andrew Cropper, Rolf Morel | 86 | cites it |
| 2021-11-09 | [Inductive logic programming at 30](https://doi.org/10.1007/s10994-021-06089-1) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Andrew Cropper, Sebastijan Dumančić, Richard Evans et al. | 61 | cites it |
| 2021-03-11 | [Beneficial and harmful explanatory machine learning](https://doi.org/10.1007/s10994-020-05941-0) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Lun Ai, Stephen Muggleton, Céline Hocquette et al. | 34 | cites it |
| 2018-04-23 | [Learning efficient logic programs](https://doi.org/10.1007/s10994-018-5712-6) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Andrew Cropper, Stephen Muggleton | 33 | cites it |
| 2019-11-20 | [Logical reduction of metarules](https://doi.org/10.1007/s10994-019-05834-x) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Andrew Cropper, Sophie Tourret | 29 | cites it |
| 2019-11-18 | [Inductive general game playing](https://doi.org/10.1007/s10994-019-05843-w) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Andrew Cropper, Richard Evans, Mark Law | 26 | cites it |
| 2023-03-14 | [$$\\alpha$$ILP: thinking visual scenes as differentiable logic programs](https://doi.org/10.1007/s10994-023-06320-1) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Hikaru Shindo, Viktor Pfanschilling, Devendra Singh Dhami et al. | 21 | cites it |
| 2019-12-03 | [Learning higher-order logic programs](https://doi.org/10.1007/s10994-019-05862-7) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Andrew Cropper, Rolf Morel, Stephen Muggleton | 11 | cites it |
| 2023-06-28 | [Explanatory machine learning for sequential human teaching](https://doi.org/10.1007/s10994-023-06351-8) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Lun Ai, Johannes Langer, Stephen Muggleton et al. | 6 | cites it |
| 2023-08-07 | [Learning logic programs by explaining their failures](https://doi.org/10.1007/s10994-023-06358-1) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Rolf Morel, Andrew Cropper | 4 | cites it |
| 2022-04-01 | [Lifting symmetry breaking constraints with inductive logic programming](https://doi.org/10.1007/s10994-022-06146-3) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Alice Tarzariol, Martin Gebser, Konstantin Schekotihin | 3 | cites it |
| 2022-04-15 | [Meta-interpretive learning as metarule specialisation](https://doi.org/10.1007/s10994-022-06156-1) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Stassa Patsantzis, Stephen Muggleton | 3 | cites it |
| 2025-05-20 | [Learning answer set programs with aggregates via sampling and genetic programming](https://doi.org/10.1007/s10994-025-06780-7) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Damiano Azzolini | 3 | cites it |
| 2024-07-03 | [Rule learning by modularity](https://doi.org/10.1007/s10994-024-06556-5) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 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

```bibtex
@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}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).