Knowledge Graphs and LLMs in Action, Video Edition

Combine knowledge graphs with large language models to deliver powerful, reliable, and explainable AI solutions.

Knowledge graphs model relationships between the objects, events, situations, and concepts in your domain so you can readily identify important patterns in your own data and make better decisions. Paired up with large language models, they promise huge potential for working with structured and unstructured enterprise data, building recommendation systems, developing fraud detection mechanisms, delivering customer service chatbots, or more. This book provides tools and techniques for efficiently organizing data, modeling a knowledge graph, and incorporating KGs into the functioning of LLMs—and vice versa.

In Knowledge Graphs and LLMs in Action 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
Build knowledge graphs from unstructured data sources using LLMs
Use machine learning algorithms to complete your graphs and derive insights from it
Reason on the knowledge graph and build KG-powered RAG systems for LLMs

In Knowledge Graphs and LLMs in Action, you’ll discover the theory of knowledge graphs then put them into practice with LLMs to build working intelligence systems. You’ll learn to create KGs from first principles, go hands-on to develop advisor applications for real-world domains like healthcare and finance, build retrieval augmented generation for LLMs, and more.

About the Technology
Using knowledge graphs with LLMs reduces hallucinations, enables explainable outputs, and supports better reasoning. By naturally encoding the relationships in your data, knowledge graphs help create AI systems that are more reliable and accurate, even for models that have limited domain knowledge.

About the Book
Knowledge Graphs and LLMs in Action shows you how to introduce knowledge graphs constructed from structured and unstructured sources into LLM-powered applications and RAG pipelines. Real-world case studies for domain-specific applications—from healthcare to financial crime detection—illustrate how this powerful pairing works in practice. You’ll especially appreciate the expert insights on knowledge representation and reasoning strategies.

What's Inside
Design knowledge graphs for real-world needs
Build KGs from structured and unstructured data
Apply machine learning to enrich, complete, and analyze graphs
Pair knowledge graphs with RAG systems

About the Reader
For ML and AI engineers, data scientists, and data engineers. Examples in Python.

About the Authors
Alessandro Negro is Chief Scientist at GraphAware and author of Graph-Powered Machine Learning. Vlastimil Kůs, Giuseppe Futia, and Fabio Montagna are seasoned ML and AI professionals specializing in Knowledge Graphs, Large Language Models, and Graph Neural Networks.