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EbookBell Team
5.0
48 reviewsKnowledge graphs help understand relationships between the objects, events, situations, and concepts in your data so you can readily identify important patterns and make better decisions. This book provides tools and techniques for efficiently labeling data, modeling a knowledge graph, and using it to derive useful insights.
In Knowledge Graphs Applied you will learn how to
• Model knowledge graphs with an iterative top-down approach based in business needs
• Create a knowledge graph starting from ontologies, taxonomies, and structured data
• Use machine learning algorithms to hone and complete your graphs
• Build knowledge graphs from unstructured text data sources
• Reason on the knowledge graph and apply machine learning algorithms
Move beyond analyzing data and start making decisions based on useful, contextual knowledge. The cutting-edge knowledge graphs (KG) approach puts that power in your hands. In Knowledge Graphs Applied, you’ll discover the theory of knowledge graphs and learn how to build services that can demonstrate intelligent behavior. You’ll learn to create KGs from first principles and go hands-on to develop advisor applications for real-world domains like healthcare and finance.
About the technology
Knowledge graphs represent a network of real-world entities—from people and places to genes and proteins—and model the relationships between them. KGs represent a real paradigm shift in the way that machines can understand data by effectively modeling the contextual information that’s vital for human knowledge. They’re poised to help revolutionize data analysis and machine learning, with applications ranging from search engines to e-commerce and more.
About the book
Knowledge Graphs Applied is a practical guide to putting knowledge graphs into action. It’s full of techniques and code samples for building and analyzing knowledge graphs, all demonstrated with serious full-sized datasets.
