Heterogenous data holds significant inherent context. We would like our machine learning models to understand this context, and utilise this ancilliary but critical information to improve the accuracy and versatility of our models.
How can we systematically make use of context in Machine Learning?
We delve in and investigate the knowledge modelling techniques, which applied with the right ML strategies, give us a promising approach for robustly handling heterogeneous data in large knowledge models. We aim to do this in a way that allows us to build any Machine Learning models, including graph learning models like our KGCN.
James comes from a background of Computer Vision, specializing in automated diagnostics. He is now the Principal Scientist at TypeDB. His mission is to demonstrate to the world how traditional symbolic approaches to AI, built-in to TypeDB, can be combined with present-day research in machine learning.
To this end, he manages Vaticle’s centre of research projects, KGLIB. This library has two purposes: to facilitate building advanced intelligent systems atop of a Knowledge Graph, and to inspire the Vaticle community with new approaches of doing so.