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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 (KGCN): a method for performing machine learning over a Grakn Knowledge Graph, which captures micro-context and macro-context for any Concept within the graph.
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