How Graph Data Science can turbocharge your Knowledge Graph

Knowledge Graphs are becoming mission-critical across many industries. More recently, we are witnessing the application of Graph Data Science to Knowledge Graphs, offering powerful outcomes. But how do we define Knowledge Graphs in industry and how can they be useful for your project? In this talk, we will illustrate the various methods and models of Graph Data Science being applied to Knowledge Graphs and how they allow you to find implicit relationships in your graph which are impossible to detect in any other way. You will learn how graph algorithms from Betweenness centrality to Embeddings drive ever deeper insights in your data.

• Understanding how implicit relationships can be detected in a Knowledge graph
• What graph data science, how graph embeddings are the bridge to classical machine learning
• How Graph data science enhances Knowledge Graphs

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Kristof Neys – Director Graph Data Science Technology | Neo4j

Kristof is a Graph Data Scientist in the Field Engineering team at Neo4j, the leading graph technology platform, where he advises on and implements graph data science solutions for Neo4j’s clients. He is also currently pursuing a PhD in Graph Machine Learning at the University of London, Birkbeck. Kristof holds a MSc in Mathematics and a MSc in Data Science from the University of London. Prior to joining Neo4j Kristof had a 20 year career in Fixed Income Sales & Trading at some of the major investment banks in London.