Knowledge Graph Convolutional Networks

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# Knowledge Graph Convolutional Networks

As humans we use our knowledge, our reasoning and our understanding of situational context to make accurate predictions about the world around us; machine learning doesn’t typically make use of any of this rich information.

The ability to leverage highly interrelated data will yield a step-change in the quality and complexity of predictions that can be made for the same volume of data.

We present Knowledge Graph Convolutional Networks: a method for performing machine learning over a Grakn Knowledge Graph, which captures micro-context and macro-context for any Concept within the graph.

This methodology demonstrates how we can usably combine knowledge, learning and reasoning to build systems that start to look truly intelligent.

# James Fletcher, Principal Scientist, Grakn Labs

James is the Principal Scientist at Grakn, primarily working on educating the world on how to use a knowledge graph such as Grakn to build cognitive/intelligent systems. For this he is implementing examples as templates and ideas for how clients and community members can innovative in their own specific projects.
With a background in Computer Vision, having co-founded his own startup in veterinary diagnostics, James's priority is to research the new kinds of intelligent system that are enabled by using Grakn as a knowledge graph