SKU/Artículo: AMZ-B0G2X99SBM
Knowledge Graphs and Hybrid AI: How to Integrate Semantic Structures with Large Language Models for Reliable and Grounded Intelligence
Format:
Kindle
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0.79 kg
0.79 kg
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Sí
Sí
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Nuevo
Nuevo
Producto de:
Amazon
Amazon
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USA
USA
Sobre este producto
- Bridge the gap between structure and intelligence. In Knowledge Graphs and Hybrid AI, Jude Voss explores how to combine semantic reasoning, graph-based data modeling, and large language models (LLMs) to create AI systems that are reliable, explainable, and grounded in truth. Modern LLMs excel at generating language but often lack contextual precision and factual consistency. Knowledge graphs, on the other hand, bring structure, transparency, and reasoning to complex data ecosystems. This book shows you how to integrate both worlds — building hybrid architectures that combine the creativity of LLMs with the rigor of graph-based knowledge representation. Through real-world use cases, hands-on frameworks, and Python-based examples, you’ll learn to design, build, and deploy knowledge-driven AI systems for domains like finance, healthcare, and enterprise intelligence. Whether you’re designing retrieval-augmented generation (RAG) pipelines, constructing ontologies, or building reasoning agents, this guide provides a complete technical foundation. Inside you’ll learn how to:Model and enrich knowledge graphs from structured and unstructured dataIntegrate KGs with LLMs for grounded, explainable AIApply graph reasoning and vector retrieval to enhance RAG systemsBuild hybrid architectures that reduce hallucinations and improve reliabilityUse tools like LangChain, LangGraph, Neo4j, and GraphQL in real-world deploymentsBuild the next generation of intelligent systems — where knowledge meets understanding
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