How Graph Data Science can turbocharge your Knowledge Graph – Kristof Neys, Neo4j

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Session Outline

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.

Key Takeaways

– 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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