3 papers
cs.AI2026
The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning
Agnese Chiatti, Michael Cochez, Cristina Cornelio +14
Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention. They complement the data-intensive statistical approaches of neural n…
cs.AI2026
Autoregressive Models for Knowledge Graph Generation
Thiviyan Thanapalasingam, Antonis Vozikis, Peter Bloem +1
Knowledge Graph (KG) generation requires models to learn complex semantic dependencies between triples while maintaining domain validity constraints. Unlike link prediction, which…
cs.LG2025
GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks
Taraneh Younesian, Daniel Daza, Emile van Krieken +2
Graph neural networks (GNNs) learn to represent nodes by aggregating information from their neighbors. As GNNs increase in depth, their receptive field grows exponentially, leading…