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 Jes�s 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

About the Authors

Dr. Jesus Barrasa, an expert in semantic technologies and graph databases, is head of the solutions architecture team in EMEA at Neo4j and leads the development of neosemantics (a Neo4j plugin for RDF). He cowrote Knowledge Graphs: Data in Context for Responsive Businesses (O'Reilly) and is cohost of the Going Meta live webcast.

Dr. Jim Webber is chief scientist at Neo4j, where he works on fault-tolerant graph databases. He coauthored Graph Databases for Dummies (Wiley) and Graph Databases and Knowledge Graphs: Data in Context for Responsive Businesses, both for O'Reilly. He's also a visiting professor at Newcastle University, UK.

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