3 citations · 4 across the 5 of their papers we have counts for
5 papers
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…
The Past and Future of AI Scientists
Ross D. King
We present a survey of the past and future of AI Scientists: machines capable of automating science. AI Scientists can originate hypotheses, deduce their consequences, design and e…
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…
Genesis: Towards the Automation of Systems Biology Research
Ievgeniia A. Tiukova, Daniel Brunnsåker, Erik Y. Bjurström +8
The cutting edge of applying AI to science is the closed-loop automation of scientific research: robot scientists. We have previously developed two robot scientists: `Adam' (for ye…
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…