Abstract: The Neo4j Graph Database is rapidly becoming one of the leading solution for graph data analysis both in the industrial and in the academic field. Scalability, performance and ease of use are the founding points of its success as a data storage and enrichment tool. The integration with the Graph Data Science library, in particular, provides a streamlined way to connect graph representations with machine learning algorithms designed to extract latent knowledge. In this tutorial, we will cover the main aspects of knowledge graph design, its general management, performance optimisation aspects and connection with Natural Language Processing tools (in Python).
Short Bio: Phd Computer Sciences by Politecnico di Milano. Marco is responsible for pre-sales activities for the Italian market. He loves switching on light bulbs with people, facilitating those ‘aha moments’ that change mindsets and open up new possibilities. Marco helps to identify customer requirements, designs a product/service to meet those requirements, evangelizes the proposed solution, and finally adjusts the solution as necessary to become relevant, effective, right-fit, and complete for the customer needs. He is passionately responsible for the collaboration with major Italian universities focusing on new research topics on graph databases and related use cases.