Description

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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 Jesus 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 Author

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Dr. Jesus Barrasa - Jesus leads the Sales Engineering team in EMEA and is Neo4j's resident expert in Semantic technologies. He co-wrote Knowledge Graphs: Data in Context for Responsive Businesses (O'Reilly Report) and leads the development of Neosemantics (Neo4j plugin for RDF). Prior to joining Neo4j, Jesus worked for data integration companies like Denodo and Ontology Systems(now EXFO) where he got first-hand experience with many successful large Graph Technology projects for major companies all over the world. Jesus' Ph.D. is in Artificial Intelligence/Knowledge Representation, focused on the automatic repurposing of legacy relational data as Knowledge Graphs.Dr. Maya Natarajan - Maya is Sr Director, Knowledge Graphs. At Neo4j, Maya is responsible for the 'go-to-market' strategy for knowledge graphs. She is the in-house knowledge graph expert and was a major contributor to Knowledge Graphs: Data in Context for Responsive Businesses (O'Reilly Report). Maya has positioned various technologies from blockchain to predictive and user-based analytics to machine learning to deep learning and search in a myriad of industries including Life Sciences, Financial Services, Supply Chain, and Manufacturing at various large and small organizations. Maya has a Ph.D. in Chemical Engineering from Rice University and started her career in biotechnology, where she has five patents to her name.Dr. Jim Webber - Jim is Neo4j's Chief Scientist and Visiting Professor at Newcastle University, UK. At Neo4j, Jim works on fault-tolerant graph databases and co-wrote Graph Databases (1st and 2nd editions, O'Reilly), Graph Databases for Dummies (Wiley), and Knowledge Graphs: Data in Context for Responsive Businesses (O'Reilly Report). Jim has a long history of work on fault-tolerant distributed systems and often advises customers on issues of scale, performance, and fault tolerance for their data-intensive systems.