How to Build a Knowledge Graph in 7 Steps

Knowledge graphs, built on graph databases, offer a solution to the "data model problem" by capturing relationships and business rules directly in the data structure, unlike traditional relational databases, which often bury these in SQL code. These graphs consist of nodes (data entities), relationships, and organizing principles that provide a flexible way to manage highly connected data. To build a knowledge graph, one must define its use case, choose a suitable database management system (either triple stores or property graph databases), model the data, prepare and ingest data into the graph, and then test and optimize the graph's performance. Real-world applications of knowledge graphs include enhancing recommendation engines, fraud detection, and enterprise search, as demonstrated by organizations like NASA and Cisco. These graphs evolve over time to accommodate new data and business needs, offering a scalable and efficient method for uncovering insights in complex datasets.