Building Knowledge Graphs

Building Knowledge Graphs

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

Building Knowledge Graphs

Knowledge Graphs

A rigorous and comprehensive textbook covering the major approaches to knowledge graphs, an active and interdisciplinary area within artificial intelligence. The field of knowledge graphs, which allows us to model, process, and derive insights from complex real-world data, has emerged as an active and interdisciplinary area of artificial intelligence over the last decade, drawing on such fields as natural language processing, data mining, and the semantic web. Current projects involve predicting cyberattacks, recommending products, and even gleaning insights from thousands of papers on COVID-19. This textbook offers rigorous and comprehensive coverage of the field. It focuses systematically on the major approaches, both those that have stood the test of time and the latest deep learning methods.