5 papers · 1 filter
Goal-Driven Reasoning in DatalogMTL with Magic Sets
Shaoyu Wang, Kaiyue Zhao, Dongliang Wei +4
DatalogMTL is a powerful rule-based language for temporal reasoning. Due to its high expressive power and flexible modeling capabilities, it is suitable for a wide range of applica…
Structural Preservation and the Logical Expressiveness of Graph Neural Networks
PrzemysÅaw Andrzej WaÅÄga, Bernardo Cuenca Grau
Bridges between graph neural networks (GNNs) and logical formalisms have been established by fixing architectural choices, such as the types of aggregation, combination, and activa…
The Correspondence Between Bounded Graph Neural Networks and Fragments of First-Order Logic
Bernardo Cuenca Grau, Eva Feng, PrzemysÅaw Andrzej WaÅÄga
Graph Neural Networks (GNNs) address two key challenges in applying deep learning to graph-structured data: they handle varying size input graphs and ensure invariance under graph…
Aggregate-Combine-Readout GNNs Are More Expressive Than Logic C2
Stan P Hauke, PrzemysÅaw Andrzej WaÅÄga
In recent years, there has been growing interest in understanding the expressive power of graph neural networks (GNNs) by relating them to logical languages. This research has been…
Fuzzy Datalog over Arbitrary t-Norms
Matthias Lanzinger, Stefano Sferrazza, PrzemysÅaw A. WaÅÄga +1
One of the main challenges in the area of Neuro-Symbolic AI is to perform logical reasoning in the presence of both neural and symbolic data. This requires combining heterogeneous…