Building Knowledge Graphs. A Practitioner's Guide 197722

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 saving interlinked descriptions of entities (objects, events, situations, or abstract concepts) while encoding the semantics underlying the terminology. How do you create a knowledge graph? And how do you move it from theory into practice?

Using hands-on examples, this practical book shows data scientists and data practitioners how to build their own custom knowledge graphs. Authors Jesus Barrasa, Maya Natarajan, and Jim Webber from Neo4j illustrate patterns commonly used for building knowledge graphs that solve many of today's pressing problems. You'll quickly discover how these graphs become exponentially more useful as you add more data.

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

Understand what pattern detection knowledge graphs help you accomplish

Explore dependency knowledge graphs through examples

Use examples of natural language knowledge graphs and chatbots

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.