Answer Set Programming (ASP) and Large Language Models (LLMs) offer complementary strengths in symbolic reasoning and natural language understanding. In this work, we present an integrated framework for reliable structured knowledge extraction from natural language that combines the syntactic capabilities of LLMs with...
Mario Alviano, Matteo Capalbo, G. Gottlob et al.· Journal of Artificial Intell...· 0 citations
This work presents a data-driven framework designed to bridge the gap between interactive exploration and formal logic reasoning, which allows researchers to interactively validate complex hypotheses and uncover latent clusters that would be difficult to identify through traditional imperative filtering.
Mario Alviano, Eleonora Bilotta, Pierpaolo Sestito· 0 citations
An empirical evaluation shows that ALM–ASP significantly improves both syntactic validity and end-to-end correctness over general-purpose LLM baselines, and also achieves improved instance coverage compared to the closest agentic alternative, CP–Agent.
L. A. R. Reiners, Alice Tarzariol, Mario Alviano et al.· Proceedings of the TwentyThi...· 0 citations
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