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
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 :
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Symbolic AI : Raisonnement par règles et ontologies (OWL/RDF)
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Neural AI : Transformers pour NER, sentiment, cohérence
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IR Engine : Recherche d’évidence (BM25, TF-IDF, TREC)
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GraphRAG : Mémoire contextuelle par graphe de connaissances
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E-E-A-T : Scoring qualité Google (Experience, Expertise, Authority, Trust)
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Technologies: Python, NLP, OWL Ontologies, Machine Learning, Neuro-symbolic AI
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Status: Active Research (Doctoral Project)
systemFactChecking_Sandbox/
├── syscred/ # ← Package Python unique et propre
│ ├── __init__.py
│ ├── backend_app.py # API Flask
│ ├── verification_system.py # Système principal
│ ├── config.py
│ ├── ner_analyzer.py # ← À restaurer
│ ├── eeat_calculator.py # ← À restaurer
│ ├── graph_rag.py
│ ├── ontology_manager.py
│ ├── api_clients.py
│ ├── seo_analyzer.py
│ ├── ir_engine.py
│ ├── eval_metrics.py
│ ├── trec_retriever.py
│ ├── trec_dataset.py
│ ├── liar_dataset.py
│ ├── database.py
│ └── static/
│ └── index.html
├── huggingface_space/
│ ├── Dockerfile # ← Mis à jour
│ └── README.md
├── requirements.txt # ← Allégé pour Render
├── requirements-full.txt # ← Version complète pour HF/local
├── Dockerfile # Pour Render
├── .env
├── README.md
├── 03_Docs/
├── 99_Archive/ # ← Anciennes versions archivées
└── ...Le Léviathan Algorithmique : pouvoir, opacité et responsabilité à l'ère de l'intelligence artificielle
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Modeling a Hybrid System for Verifying Information Credibility (2025)
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Hybrid System Ontology for Information Source Verification (2025)
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
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
OWL ontology for modeling information verification systems.
- Technologies: OWL, RDF, Protégé
- Status: Published
- 📂 Repository | 📄 Paper
Neural machine translation system with attention mechanisms.
- Technologies: Python, TensorFlow, PyTorch, NMT
- Status: Completed
- 📂 Repository | 📄 Paper
Research & Productivity Tools:
- 🔬 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
- 🌐 Website: dominiqueloyer.github.io
- 📧 Email: 2e2g3zhvt@mozmail.com
- 💼 LinkedIn: linkedin.com/in/dominique-loyer-456ab739b
- 🔬 ORCID: 0009-0003-9713-7109
- 📚 Google Scholar: Profile
- 🔍 ResearchGate: Dominique Loyer
Unless otherwise specified, research code is released under MIT License.
Academic publications follow their respective copyright agreements.
⭐ If you find my work interesting, consider following or starring relevant repositories!



