Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
  • Academic publication links
  • Academic email domains
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  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (11.0%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: Wimmics
  • License: agpl-3.0
  • Language: TypeScript
  • Default Branch: master
  • Size: 2.3 MB
Statistics
  • Stars: 0
  • Watchers: 4
  • Forks: 0
  • Open Issues: 1
  • Releases: 1
Created about 1 year ago · Last pushed 8 months ago
Metadata Files
Readme License Citation Codemeta

README.md

Q²Forge

This project provides an end-to-end pipeline to generate a dataset of (question, SPARQL query) pairs for a given Knowledge Graph (KG). The backend (Gen²KGBot) can be found at this repo.

Features

It provides the following feature: - KG Configuration and Pre-processing (read more). - Competency question generator (read more). - SPARQL query generator (read more). - SPARQL query refinement (read more).

The following diagram shows the pipeline and how Q²Forge integrates with other systems

Q²Forge pipeline

Demo video ⏯️

Teaser

Q²Forge teaser video

Full tutorial

Q²Forge full video

Run locally

To run locally you need to:

1) Install the dependencies: run the command:

bash npm run i

2) Start a local development server: run the command:

bash ng serve 3) Visualise the result: once the server is running, open your browser and navigate to http://localhost:4200/.

Build & Deploy

To build the project run the command:

bash npm run dc

This will run the following command: bash ng build --base-href=/q2forge/ --deploy-url=/q2forge/ --configuration=production

which will compile the project and store the build artifacts in the dist/q2forge directory.

License

See the LICENSE file.

Cite this work

Yousouf TAGHZOUTI, Franck MICHEL, Tao JIANG, Louis-Félix NOTHIAS, Fabien GANDON (2025). Q²Forge. https://github.com/Wimmics/q2forge

See BibTex @software{taghzouti_q2forge:2025, author = {TAGHZOUTI, Yousouf and MICHEL, Franck and JIANG, Tao and NOTHIAS, Louis-Félix , and GANDON Fabien}, title = {{Q²Forge}}, url = {https://github.com/Wimmics/q2forge}, version = {1.0}, year = {2025} }

Owner

  • Name: Wimmics
  • Login: Wimmics
  • Kind: organization

Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: 'Q²Forge'
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Yousouf
    family-names: Taghzouti
    email: yousouf.taghzouti@univ-cotedazur.fr
    affiliation: 'Univ. Côte d''Azur, Inria, ICN, I3S, France'
    orcid: 'https://orcid.org/0000-0003-4509-9537'
    
  - given-names: Franck
    family-names: Michel
    email: franck.michel@inria.fr
    affiliation: 'Univ. Côte d''Azur, CNRS, Inria'
    orcid: 'https://orcid.org/0000-0001-9064-0463'
    
  - given-names: Tao
    family-names: Jiang
    email: tao.jiang@cnrs.fr
    affiliation: 'Univ. Côte d''Azur, CNRS, ICN, France'
    orcid: 'https://orcid.org/0000-0002-5293-3916'
    
  - given-names: Louis-Felix
    family-names: Nothias
    email: louis-felix.nothias@cnrs.fr
    affiliation: 'Univ. Côte d''Azur, CNRS, ICN, France'
    orcid: 'https://orcid.org/0000-0001-6711-6719'
    
  - given-names: Fabien
    family-names: Gandon
    email: fabien.gandon@inria.fr
    affiliation: 'Inria, Univ. Côte d''Azur, CNRS, I3S, France'
    orcid: 'https://orcid.org/0000-0003-0543-1232'
    
repository-code: 'https://github.com/Wimmics/q2forge'
license: AGPL-3.0-or-later
version: 1.0
date-released: '2025-05-12'

CodeMeta (codemeta.json)

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  "description": "QForge addresses the challenge of generating new competency questions for a KG and corresponding SPARQL queries that reflect those questions. It iteratively validates those queries with human feedback and LLM as a judge.",
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