Incredibly useful, knowledge graphs help organizations keep track of medical research, cybersecurity threat intelligence, GDPR compliance, web user engagement, and much more. They do so by storing interlinked descriptions of entities—objects, events, situations, or abstract concepts—and encoding the underlying information. How do you create a knowledge graph? And how do you move it from theory into production?
Using hands-on examples, this practical book shows data scientists and data engineers how to build their own knowledge graphs. Authors Jesús Barrasa and Jim Webber from Neo4j illustrate common patterns for building knowledge graphs that solve many of today's pressing knowledge management problems. You'll quickly discover how these graphs become increasingly useful as you add data and augment them with algorithms and machine learning.
Learn the organizing principles necessary to build a knowledge graph
Explore how graph databases serve as a foundation for knowledge graphs
Understand how to import structured and unstructured data into your graph
Follow examples to build integration-and-search knowledge graphs
Learn what pattern detection knowledge graphs help you accomplish
Explore dependency knowledge graphs through examples
Use examples of natural language knowledge graphs and chatbots
Use graph algorithms and ML to gain insight into connected data