Engineering Knowledge Graph Data for a Semantic Recommendation AI System


Semantic recommendation systems are a type of AI system that can help surface content in vast repositories by representing the data as a knowledge graph and implementing graph traversal algorithms that return relevant content to end users. These systems can be very useful for clients across industries, and plenty of fun for the data engineers on-board, requiring skills such as auto-tagging, ETL pipeline construction and orchestration, and graph algorithm design and implementation. Learn how to design such a system in this in-depth tutorial.


Ethan Hamilton is a data engineer at Enterprise Knowledge, the world's largest dedicated knowledge management consultancy. His areas of expertise include semantic modeling, graph databases, and backend development. Ethan has worked with clients in tech, finance, and public safety to build semantic AI and machine learning solutions to knowledge management problems.

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