Knowledge Graphs and LLMs in Action - Softcover

Negro, Alessandro

Softcover

ISBN 10: 1633439895 ISBN 13: 9781633439894

Publisher: Manning Publications, 2025

Synopsis

Data overload, disconnected context, and stalled machine learning results are common frustrations for data teams. Even with vast datasets and advanced models, insights remain elusive when information is scattered and relationships are unclear. What if you could structure your data in a way that gives it meaning, connects the dots, and powers smarter, faster learning? By building knowledge graphs that integrate with large language models, you can transform disconnected information into actionable, context-rich intelligence that drives real results.

• Iterative top-down modeling: Aligns every graph decision with clear business questions.

• Ontology and taxonomy starters: Jump-start graph design from your existing structured data.

• Python code walk-throughs: Let you replicate techniques on day one, no guesswork.

• GNN and BERT integration: Upgrade graphs with deep learning for smarter reasoning and predictions.

• Real healthcare and policing cases: Prove scalability on messy, high-stakes datasets.

Knowledge Graphs and LLMs in Action by GraphAware scientists Dr. Alessandro Negro and colleagues delivers a code-rich softcover reference that unites cutting-edge research with field-tested engineering practice.

Starting with business questions, you model ontologies, import varied sources, then iteratively expand your graph. Later chapters layer GNNs, transformers, and reasoning algorithms, showing complete pipelines on full-scale datasets.

You will leave with repeatable workflows, reusable code, and the confidence to connect fragmented data into intelligent, context-aware applications. Stop guessing; start delivering measurable machine learning impact.

About this title

About the Author

Dr. Alessandro Negro is the Chief Scientist at GraphAware. 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 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. He studied Graph Representation Learning techniques to support the automatic building of Knowledge Graphs.

Fabio Montagna is the Lead Machine Learning Engineer at GraphAware. As a bridge between science and industry, he assists with moving rapidly from scientific reasoning to product value.

From the Back Cover

From the back cover:

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.

Edition details

• Publisher: Manning Publications
• Publication date: 2025
• Language: English
• ISBN 10: 1633439895
• ISBN 13: 9781633439894
• Binding: Paperback
• Edition number: 1
• Number of pages: 472