Related projects
Discover more projects across a range of sectors and discipline — from AI to cleantech to social innovation.
Knowledge graphs store facts using relations between pairs of entities. In this work, we address the question of link prediction in knowledge graphs. Our general approach broadly follows neighborhood aggregation schemes such as that of Graph Convolutional Networks (GCN), which in turn was motivated by spectral graph convolutions. Our proposed model will aggregate information from neighbour entities and relations. Contrary to most existing knowledge graph completion methods, our model is expected to work in the inductive setting: Predicting relations for entities not seen during training.
David Poole
Bahare Fatemi
Element AI
Computer science
Professional, scientific and technical services
Accelerate
Discover more projects across a range of sectors and discipline — from AI to cleantech to social innovation.
Find the perfect opportunity to put your academic skills and knowledge into practice!
Find ProjectsThe strong support from governments across Canada, international partners, universities, colleges, companies, and community organizations has enabled Mitacs to focus on the core idea that talent and partnerships power innovation — and innovation creates a better future.