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Finally, PhD candidate in AI after ✅the synthesis exam on Nov.26 2025 at UQAM🎓
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Finally, PhD candidate in AI after ✅the synthesis exam on Nov.26 2025 at UQAM🎓
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DominiqueLoyer/README.md

👨‍🔬 Dominique Loyer

PhD Candidate in Cognitive Informatics at Université du Québec à Montréal (UQAM)

Research Interests:

  • 🤖 Algorithmic Bureaucracy & Governance
  • 🔍 Explainable AI (xAI) & Transparency
  • 📊 Recommendation Systems & Collaborative Filtering
  • 🌐 Digital Sociology & Information Credibility
  • ⚖️ AI Ethics & Accountability

💖 Support My GITHUB Open Projects

SysCRED — Système Neuro-Symbolique de Vérification de Crédibilité

[

PhD Thesis Prototype — Dominique S. Loyer (UQAM)
Citation Key: loyerModelingHybridSystem2025

[!NOTE] **Version stable : v2.4.1 (12 mars 2026) — (dashboard explainers, TREC metrics, GraphRAG)**
>  - **Fact-Checking** multi-sources (Google Fact Check API)
>  - **E-E-A-T** (Experience, Expertise, Authority, Trust)
>  - **NER** — Extraction d'entités nommées (spaCy)
>  - **GraphRAG** — Réseau Neuro-Symbolique (D3.js)
>  - **Métriques** — Precision, Recall, nDCG, MRR
>  - **Bias Analysis** — Détection de biais

Site institutionnel: https://syscred.uqam.ca

🔍 systemFactChecking – Hybrid Information Credibility Verification System

A hybrid fact-checking system combining predicate-logic rules, ontologies (OWL), and neuro-symbolic AI to evaluate the credibility of information sources.

SysCRED est un système hybride de fact-checking combinant :

  • Symbolic AI : Raisonnement par règles et ontologies (OWL/RDF)

  • Neural AI : Transformers pour NER, sentiment, cohérence

  • IR Engine : Recherche d’évidence (BM25, TF-IDF, TREC)

  • GraphRAG : Mémoire contextuelle par graphe de connaissances

  • E-E-A-T : Scoring qualité Google (Experience, Expertise, Authority, Trust)

  • Technologies: Python, NLP, OWL Ontologies, Machine Learning, Neuro-symbolic AI

  • Status: Active Research (Doctoral Project)

  • 📂 Repository

  • 📄 Modeling Paper

  • 📄 Ontology Paper

  • 📄 Beamer Presentation - (PDF)

refactorisation de sysCRED (14 mars 2026)

systemFactChecking_Production/
├── src/
│   └── syscred/                    ← Package Python principal
│       ├── __init__.py
│       ├── core/                   ← Cœur du système
│       │   ├── config.py           ← Configuration centralisée
│       │   ├── verification_system.py
│       │   └── scoring.py
│       ├── api/                    ← Interface web
│       │   ├── backend_app.py      ← Flask app
│       │   ├── routes/
│       │   └── middleware/
│       ├── ml/                     ← Modèles ML
│       │   ├── sentiment.py
│       │   ├── ner_analyzer.py     ← À fusionner depuis HF
│       │   └── eeat_calculator.py  ← À fusionner depuis HF
│       ├── graph/                  ← GraphRAG & Ontologie
│       │   ├── graph_rag.py
│       │   ├── ontology_manager.py
│       │   └── queries.py
│       ├── ir/                     ← TREC & Information Retrieval
│       │   ├── ir_engine.py
│       │   ├── trec_retriever.py
│       │   └── eval_metrics.py
│       ├── datasets/               ← Gestion datasets
│       │   └── liar_dataset.py
│       └── utils/                  ← Utilitaires
│           ├── api_clients.py
│           ├── database.py
│           └── helpers.py
│
├── data/                           ← Données (hors git pour gros fichiers)
│   ├── ontology/
│   │   ├── sysCRED_onto26avrtil.ttl
│   │   └── sysCRED_data.ttl
│   └── datasets/
│       ├── liar/                   ← Dataset LIAR
│       └── trec/                   ← Corpus TREC AP88-90 (276MB)
│
├── docs/                           ← Documentation
│   ├── DEV_LOGS/                   ← Journaux de développement
│   ├── PUBLICATIONS/               ← Papers et présentations
│   └── API.md
│
├── tests/                          ← Tests
│   ├── unit/
│   ├── integration/
│   └── benchmarks/
│
├── notebooks/                      ← Jupyter notebooks
│   └── exploration/
│
├── deploy/                         ← Configurations déploiement
│   ├── huggingface/                ← Spécifique HF Space
│   │   ├── Dockerfile.hf
│   │   ├── requirements-hf.txt
│   │   └── static/                 ← Assets HF
│   └── docker/                     ← Docker générique
│       ├── Dockerfile
│       └── docker-compose.yml
│
├── scripts/                        ← Scripts utilitaires
│   ├── start_syscred.sh
│   ├── deploy_huggingface.sh
│   └── run_benchmarks.py
│
├── 99_Archive/                     ← ARCHIVES (jamais supprimées)
│   └── 2026-03-14_restructuration/
│       ├── systemFactChecking-1/   ← Copie complète préservée
│       ├── syscred-space_backup/   ← Version HF préservée
│       ├── v2_syscred/             ← Ancienne version
│       ├── syscred_legacy/
│       └── SysCRED_v2.1_Update/
│       └── README.md               ← Documentation des archives
│
├── pyproject.toml                  ← Configuration package Python
├── requirements.txt
├── requirements-dev.txt
├── .env.example
├── .gitignore                      ← Mis à jour avec data/large
└── README.md                       ← Mis à jour

📚 Featured Publications

🔍 **sysCRED: ** (2026)

  • Proposition de recherche doctorale (DIC-9411) : -Architecture, formalisation et implantation du système sysCRED
  • Une approche hybride neuro-symbolique pour la crédibilité et le raisonnement en informatique cognitive

    SysCRED — Système Neuro-Symbolique de Vérification de Crédibilité

PhD Thesis Prototype — Dominique S. Loyer (UQAM)
Citation Key: loyerModelingHybridSystem2025

[!NOTE] **Version stable : v2.4.1 (12 mars 2026) — (dashboard explainers, TREC metrics, GraphRAG)**
>  - **Fact-Checking** multi-sources (Google Fact Check API)
>  - **E-E-A-T** (Experience, Expertise, Authority, Trust)
>  - **NER** — Extraction d'entités nommées (spaCy)
>  - **GraphRAG** — Réseau Neuro-Symbolique (D3.js)
>  - **Métriques** — Precision, Recall, nDCG, MRR
>  - **Bias Analysis** — Détection de biais

🏆 PhD (Examen de synthèse doctoral)

Le Léviathan Algorithmique : pouvoir, opacité et responsabilité à l'ère de l'intelligence artificielle

  • Technologies: Python, NLP, Ontologies (OWL), Machine Learning

  • Status: Active Research

  • 📂 Repository

📝 Selected Papers

  • Modeling a Hybrid System for Verifying Information Credibility (2025)

  • Hybrid System Ontology for Information Source Verification (2025)

📚 TREC Evaluation System

Evaluation of Information Retrieval Models on TREC AP 88-90 (2025)

Évaluation comparative de modèles de recherche d'information sur les collections TREC AP 88-90.

Technologies : Python • Information Retrieval • BM25 • TF-IDF • Vector Space Models

Résultats :

  • 📊 Analyse de ~243,000 documents
  • 📈 Comparaison BM25 vs. VSM
  • 🎯 Métriques MAP, NDCG, Precision@K

📄 Lire l'article

📄 View all publications →

💻 Research Projects

🔍 **sysCRED: ** (2026)

  • Proposition de recherche doctorale (DIC-9411) : -Architecture, formalisation et implantation du système sysCRED

  • Une approche hybride neuro-symbolique pour la crédibilité et le raisonnement en informatique cognitive

  • Technologies: Python, NLP, Ontologies (OWL), Machine Learning

  • Status: Active Research

  • 📂 Repository

Plateforme de diffusion de contenu de l'Institut des Sciences Cognitives de l'UQAM

Zenodo | CC BY 4.0 | v1.0.4

DOI :

🔍 Modeling a Hybrid System for Verifying Information Credibility (2025)

A hybrid system combining predicate logic and ML/AI for assessing information source credibility.

  • Technologies: Python, NLP, Ontologies (OWL), Machine Learning
  • Status: Active Research
  • 📂 Repository | 📄 Paper

🌐 Hybrid System Ontology for Information Source Verification (2025)

OWL ontology for modeling information verification systems.

  • Technologies: OWL, RDF, Protégé
  • Status: Published
  • 📂 Repository | 📄 Paper

🔤 Neural Machine Translation (English-Russian)

Neural machine translation system with attention mechanisms.

  • Technologies: Python, TensorFlow, PyTorch, NMT
  • Status: Completed
  • 📂 Repository | 📄 Paper

🛠️ Technical Skills https://dominiqueloyer.github.io/dominique-loyer-skills.html

Programming Languages:

AI/ML Frameworks:

Research & Productivity Tools:

📊 GitHub Statistics

🎯 Current Focus

  • 🔬 Completing PhD dissertation on algorithmic bureaucracy
  • 📝 Publishing research on AI transparency and accountability
  • 💡 Developing explainable AI frameworks for recommendation systems
  • 🌍 Contributing to open science and reproducible research

📚 Publications Highlights

Année Titre Type DOI
2025 Le Léviathan Algorithmique : pouvoir, opacité et responsabilité à l'ère de l'intelligence artificielle Examen de synthèse doctoral
2025 Modeling a Hybrid System for Verifying the Credibility of Information Sources Preprint
2025 Hybrid System for Verifying Credibility: An Ontology Preprint
2025 Evaluation of Information Retrieval Models on TREC AP 88-90 Preprint
2025 Development and Evaluation of an English-to-Russian Neural MT System Preprint

📫 Contact & Links

📜 License

Unless otherwise specified, research code is released under MIT License.
Academic publications follow their respective copyright agreements.

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