Knowledge Graph Convolution Networks | Introducing Grakn Labs to the Bay Area | San Francisco
About this event
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.
Takeaways
- How KGCN allows for the capture of micro-context and macro-context for any concept within a knowledge graph
- Introduction to Knowledge Graphs within the context of Machine Learning