Knowledge Graphs and LLMs in Action: Build AI systems using connected data
Knowledge Graphs and LLMs in Action: Build AI systems using connected data
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Knowledge Graphs and LLMs in Action: Build AI systems using connected data
Knowledge Graphs and LLMs in Action: Build AI systems using connected data
Knowledge Graphs and LLMs in Action: Build AI systems using connected data
Knowledge Graphs and LLMs in Action: Build AI systems using connected data
товар №: 201103489

Knowledge Graphs and LLMs in Action: Build AI systems using connected data

товар №: 201103489

UZS 953113

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  • 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 needsCreate a knowledge graph starting from ontologies, taxonomies, and structured dataBuild knowledge graphs from unstructured data sources using LLMsUse machine learning algorithms to complete your graphs and derive insights from itReason on the knowledge graph and build KG-powered RAG systems for LLMsIn 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 technologyUsing 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 bookKnowledge 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 insideDesign knowledge graphs for real-world needsBuild KGs from structured and unstructured dataApply machine learning to enrich, complete, and analyze graphsPair knowledge graphs with RAG systemsAbout the readerFor ML and AI engineers, data scientists, and data engineers. Examples in Python.About the authorAlessandro 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 ContentsPart 11 Knowledge graphs and LLMs: A killer combination2 Intelligent systems: A hybrid approachPart 23 Create your first knowledge graph from ontologies4 From simple networks to multisource integrationPart 35 Extracting domain-specific knowledge from unstructured data6 Building knowledge graphs with large language models7 Named entity disambiguation8 NED with open LLMs and domain ontologiesPart 49 Machine learning on knowledge graphs: A primer approach10 Graph feature engineering: Manual and semiautomated approaches11 Graph representation learning and graph neural networks12 Node classification and link prediction with GNNsPart 513 Knowledge graph–powered retrieval-augmented generation14 Asking a KG questions with natural language15 Building a QA agent with LangGraph
Publisher Manning
Publication date November 18, 2025
Language English
Print length 472 pages
ISBN-10 1633439895
ISBN-13 978-1633439894
Item Weight 1.12 pounds (510 grams)
Dimensions 7.38 x 1.1 x 9.25 inches (18.7 x 2.8 x 23.5 cm)
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