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. Throughout the book, you'll find extensive examples and use-cases taken from healthcare, biomedicine, document archive management systems, and even law enforcement. You'll learn methodologies based on the very latest KG approaches, as well as deep learning graph techniques such as Graph Neural Networks and NLP-based tools like BERT.