Knowledge Graphs and LLMs in Action
Language: English
Published by Manning Publications Dez 2025, 2025
Series: Book 159 of 182 - In Action
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
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Add to basketItem description from seller
Neuware - 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 LLMsand 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, youll discover the theory of knowledge graphs then put them into practice with LLMs to build working intelligence systems. Youll 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 applicationsfrom healthcare to financial crime detectionillustrate how this powerful pairing works in practice. Youll 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 author Alessandro Negro is Chief Scientist at GraphAware and author of Graph-Powered Machine Learning. Vlastimil Kus, Giuseppe Futia, and Fabio Montagna are seasoned ML and AI professionals specializing in Knowledge Graphs, Large Language Models, and Graph Neural Networks. Table of Contents Part 1 1 Knowledge graphs and LLMs: A killer combination 2 Intelligent systems: A hybrid approach Part 2 3 Create your first knowledge graph from ontologies 4 From simple networks to multisource integration Part 3 5 Extracting domain-specific knowledge from unstructured data 6 Building knowledge graphs with large language models 7 Named entity disambiguation 8 NED with open LLMs and domain ontologies Part 4 9 Machine learning on knowledge graphs: A primer approach 10 Graph feature engineering: Manual and semiautomated approaches 11 Graph representation learning and graph neural networks 12 Node classification and link prediction with GNNs Part 5 13 Knowledge graphpowered retrieval-augmented generation 14 Asking a KG questions with natural language 15 Building a QA agent with LangGraph Get a free Elektronisches Buch (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.…
Seller Inventory # 9781633439894
- Title
- Knowledge Graphs and LLMs in Action
- Author
- Alessandro Negro
- Publisher
- Manning Publications Dez 2025
- Publication year
- 2025
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1633439895
- ISBN 13
- 9781633439894
- Item weight
- 816 grams
- Dimensions
- 233x186x26 mm
- Series
- Book 159 of 182: In Action
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.
"Synopsis" may belong to another edition of 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.
"About the title" may belong to another edition of this title.
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