Related projects
Discover more projects across a range of sectors and discipline — from AI to cleantech to social innovation.
Link Prediction on Knowledge Graphs with Graph Neural Networks
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.
Faculty Supervisor:
David Poole
Student:
Bahare Fatemi
Partner:
Element AI
Discipline:
Computer science
Sector:
Professional, scientific and technical services
University:
Program:
Accelerate
Current openings
Find the perfect opportunity to put your academic skills and knowledge into practice!
Find Projects
International
Load More
International
Load More
