3 papers
cs.LG2026
Hierarchy-Aware Semantic Losses for Knowledge Graph Link Prediction
Filip Kronström, Ross D. King
Knowledge graphs are often accompanied by ontological class hierarchies that encode valuable semantic information, yet many link prediction methods either ignore such hierarchies o…
cs.LG2026
Graph Neural Network based Hierarchy-Aware Embeddings of Knowledge Graphs: Applications to Yeast Phenotype Prediction
Filip Kronström, Alexander H. Gower, Daniel Brunnsåker +2
We present a method for finding hierarchy-aware embeddings of knowledge graphs (KGs) using graph neural networks (GNNs) enriched with a semantic loss derived from underlying ontolo…
cs.LG2026
The Use of AI-Robotic Systems for Scientific Discovery
Alexander H. Gower, Konstantin Korovin, Daniel Brunnsåker +6
The process of developing theories and models and testing them with experiments is fundamental to the scientific method. Automating the entire scientific method then requires not o…