4 citations · 4 across the 6 of their papers we have counts for
5 papers · 1 filter
Rule-Based Languages for Neurosymbolic AI
Stefania Dumbrava, Efthymia Tsamoura
Logic programming is increasingly used as the symbolic component of neurosymbolic AI systems. We survey the main rule-based languages in this setting, namely Datalog, answer set, a…
Symbol Grounding in Neuro-Symbolic AI: A Gentle Introduction to Reasoning Shortcuts
Emanuele Marconato, Samuele Bortolotti, Emile van Krieken +6
Neuro-symbolic (NeSy) AI aims to develop deep neural networks whose predictions comply with prior knowledge encoding, e.g. safety or structural constraints. As such, it represents…
Goal-Driven Query Answering over First- and Second-Order Dependencies with Equality
Efthymia Tsamoura, Boris Motik
In this paper we present the first goal-driven query answering technique for first- and second-order dependencies with equality. Our technique transforms the input dependencies so…
Mapping the Neuro-Symbolic AI Landscape by Architectures: A Handbook on Augmenting Deep Learning Through Symbolic Reasoning
Jonathan Feldstein, Paulius Dilkas, Vaishak Belle +1
Integrating symbolic techniques with statistical ones is a long-standing problem in artificial intelligence. The motivation is that the strengths of either area match the weaknesse…
Efficiently Learning Probabilistic Logical Models by Cheaply Ranking Mined Rules
Jonathan Feldstein, Dominic Phillips, Efthymia Tsamoura
Probabilistic logical models are a core component of neurosymbolic AI and are important in their own right for tasks that require high explainability. Unlike neural networks, logic…