5 papers
RAGEAR: Retrieval-Augmented Graph-Enhanced Academic Recommender
Francesco Granata, Lorenzo Lamazzi, Misael Mongiovì +2
We present RAGEAR (Retrieval-Augmented Graph-Enhanced Academic Recommender), a neurosymbolic recommender system for academic course recommendation. RAGEAR combines dense retrieval…
Tacit Knowledge Extraction via Logic Augmented Generation and Active Inference
Lorenzo Lamazzi, Aldo Gangemi, Alessio Giberti +4
Tacit knowledge plays a central role in human expertise, yet it remains difficult to capture, formalize, and reuse in machine-interpretable form. This challenge is especially relev…
Enhancing Retrieval-Augmented Generation with Entity Linking for Educational Platforms
Francesco Granata, Francesco Poggi, Misael Mongiovì
In the era of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) architectures are gaining significant attention for their ability to ground language generation in…
The Belief-Desire-Intention Ontology for modelling mental reality and agency
Sara Zuppiroli, Carmelo Fabio Longo, Anna Sofia Lippolis +6
The Belief-Desire-Intention (BDI) model is a cornerstone for representing rational agency in artificial intelligence and cognitive sciences. Yet, its integration into structured, s…
LEAD: LLM-enhanced Engine for Author Disambiguation
Giusy Giulia Tuccari, Lorenzo Giammei, Andrea Giovanni Nuzzolese +3
Author Name Disambiguation (AND) is a long-standing challenge in bibliometrics and scientometrics, as name ambiguity undermines the accuracy of bibliographic databases and the reli…