Knowledge graphs and LLMs in action / Giuseppe Futia, Vlastimil Kus, Fabio Montagna, Alessandro Negro.

Format:

Sound recording

Author/Creator:

Futia, Giuseppe, author.

Kus, Vlastimil, author.

Montagna, Fabio, author.

Negro, Alessandro, author.

Language:

English

Subjects (All):

Artificial intelligence--Computer programs.

Artificial intelligence.

Natural language processing (Computer science).

Physical Description:

1 online resource (1 audio file (13 hr., 05 min.))

Edition:

[First edition].

Place of Publication:

[Shelter Island, New York] : Manning Publications, 2025.

Summary:

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 K¿¯s, Giuseppe Futia, and Fabio Montagna are seasoned ML and AI professionals specializing in Knowledge Graphs, Large Language Models, and Graph Neural Networks. Quotes Comprehensive, pragmatic, definitive. - Paco Nathan, Senzing 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.

Notes:

OCLC-licensed vendor bibliographic record.

OCLC:

1550423516

Publisher Number:

9781633439894AU