Knowledge 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 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
• 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 and LLMs in Action, 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 and LLMs in Action 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.
About the reader
For readers who know the basics of machine learning. Examples in Python.
About the author
Dr. Alessandro Negro is the Chief Scientist at GraphAware. Alessandro has been a speaker at many prominent conferences and is the author of the Manning book Graph-Powered Machine Learning and several scientific publications. He is one of the creators of GraphAware Hume, a mission critical knowledge graph platform.
Dr. Vlastimil Kus is the Lead Data Scientist at GraphAware where he contributes to the development of Hume. Over the years he has gained significant experience in building and utilizing Knowledge Graphs from unstructured data using NLP and ML techniques in various domains. His current focus is NLP and Graph Machine Learning.
Dr. Giuseppe Futia is Senior Data Scientist at GraphAware and a Fellow at the Nexa Center for Internet & Society. He holds a Ph.D. in computer engineering from the Politecnico di Torino (Italy), where he explored Graph Representation Learning techniques to support the automatic building of Knowledge Graphs.
Fabio Montagna is the Lead Machine Learning Engineer at GraphAware. He holds a master’s degree in software engineering from Unisalento (Italy). As a bridge between science and industry, he assists with moving rapidly from scientific reasoning to product value.
From the Publisher
“Builds understanding both at a theoretical and a practical level.”
Corey L. Lanum, Visualization Partners
“An excellent introduction to building KG and LLM-powered applications.”
Dave Bechberger, Author of Graph Databases in Action
“Comprehensive and well thought out! The authors hit it out of the park again.”
Sujit Pal, Elsevier
why this book?
Knowledge Graphs and LLMs in Action provides a practical guide to combining structured knowledge graphs with LLMs, showing you how to build, enrich, and exploit graphs in tandem with large language models to gain better context, reasoning, and explainability.
You get hands-on techniques, code, and best practices across all stages—labeling data, modeling the graph, linking to LLM outputs—so you can apply these concepts in real systems.
The synergy helps mitigate common LLM weaknesses—like hallucinations or lack of factual grounding—by anchoring their outputs in explicit relational structure.
about Manning
Manning helps developers and tech professionals stay ahead in a fast-moving industry with expert-led books, videos, and projects. Learning never stops, but it’s hard to keep up, so we focus on content that’s practical, clear, and trusted. As an independent publisher, we adapt quickly, from pioneering early-access books to offering DRM-free eBooks. Our series, like “In Action” and “In a Month of Lunches”, reflect a commitment to making complex topics accessible.
Customer Reviews
4.6 out of 5 stars 355
4.2 out of 5 stars 29
4.7 out of 5 stars 5
4.6 out of 5 stars 24
4.7 out of 5 stars 4
4.4 out of 5 stars 12
Level of proficiency
Intermediate Intermediate Intermediate Intermediate Intermediate Advanced
About the reader
Readers need intermediate Python skills and some knowledge of machine learning. For intermediate Python programmers. For intermediate Python programmers. For data scientists and ML engineers. For data scientists and data analysts. For data scientists and machine learning engineers.
Special features
Includes liveBook with out built-in AI assistant. Includes liveBook with out built-in AI assistant. Includes liveBook with out built-in AI assistant. Includes liveBook with out built-in AI assistant. Includes liveBook with out built-in AI assistant. Includes liveBook with out built-in AI assistant.
Pages
368 344 688 456 232 520
ASIN : B0FSSX7CG2
Publisher : Manning
Accessibility : Learn more
Publication date : October 28, 2025
Language : English
File size : 32.9 MB
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 976 pages
ISBN-13 : 978-1638357858
Page Flip : Enabled
Best Sellers Rank: #471,611 in Kindle Store (See Top 100 in Kindle Store) #57 in Natural Language Processing (Books) #60 in Data Processing #96 in Natural Language Processing (Kindle Store)
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