Knowledge graphs, meet Deep Learning

Knowledge graphs generation is outpacing the ability to intelligently use the information that they contain. Octavian’s work is pioneering Graph Artificial Intelligence to provide the brains to make knowledge graphs useful.

Our neural networks can take questions and knowledge graphs and return answers. Imagine:

  • a google assistant that reads your own knowledge graph (and actually works)
  • a BI tool reads your business’ knowledge graph
  • a legal assistant that reads the graph of your case

Taking a neural network approach is important because neural networks deal better with the noise in data and variety in schema. Using neural networks allows people to ask questions of the knowledge graph in their own words, not via code or query languages.

Octavian’s approach is to develop neural networks that can learn to manipulate graph knowledge into answers. This approach is radically different to using networks to generate graph embeddings. We believe this approach could transform how we interact with databases.

Prior knowledge of Neural Networks is not required and the talk will include a simple demonstration of how a Neural Network can use graph data.

Andy Jefferson
Neural Networks and Graph AI Researcher, Octavian

Andy believes that graphs have the potential to provide both a representation of the world and a technical interface that allows us to develop better AI and to turn it rapidly into useful products. Andy combines expertise in machine learning with experience building and operating distributed software systems and an understanding of the scientific process. Before he worked as a software engineer, Andy was a chemist, and he enjoys using the tensor algebra that he learned in quantum chemistry when working on neural networks.


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