Knowledge Graph Engineering for LLM Systems: Building Structured Context, Reducing Hallucinations, and Integrating Graph-Based Intelligence into Large Language Models - Softcover
Synopsis
This book delivers a comprehensive and implementation-focused guide to building knowledge-driven AI systems that elevate the accuracy, reliability, and interpretability of Large Language Models. Designed for machine learning engineers, data architects, AI researchers, and enterprise practitioners, it provides a complete workflow for engineering Knowledge Graphs tailored for LLM-based applications.
The book begins by establishing the fundamentals of semantic data modeling, ontology development, schema design, and graph-based reasoning. Using practical examples, it demonstrates how to construct robust Knowledge Graphs that serve as structured, verifiable sources of truth for LLM pipelines.
Readers learn modern techniques for automated entity extraction, relationship discovery, schema population, and graph enrichment using advanced LLM prompting and hybrid NLP methods. The book outlines multiple integration patterns including Retrieval-Augmented Generation (RAG), multi-hop reasoning workflows, context fusion layers, and knowledge-guided agent architectures showing how to connect graph intelligence to model outputs.
A full coverage of operational considerations is included, such as scalable graph storage, query optimization, system performance, security models, version control, and governance frameworks required for production-grade KG–LLM deployments. Detailed evaluation strategies for measuring graph quality, LLM accuracy, contextual relevance, and end-to-end pipeline performance are also provided.
By bridging semantic technologies and modern AI systems, this book equips professionals to build context-aware, transparent, and highly dependable AI solutions capable of addressing hallucinations, improving explainability, and supporting critical enterprise applications.
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Knowledge Graph Engineering for LLM Systems: Building Structured Context, Reducing Hallucinations, and Integrating Graph-Based Intelligence into Large Language Models
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Paperback. Condition: new. Paperback. This book delivers a comprehensive and implementation-focused guide to building knowledge-driven AI systems that elevate the accuracy, reliability, and interpretability of Large Language Models. Designed for machine learning engineers, data architects, AI researchers, and enterprise practitioners, it provides a complete workflow for engineering Knowledge Graphs tailored for LLM-based applications.The book begins by establishing the fundamentals of semantic data modeling, ontology development, schema design, and graph-based reasoning. Using practical examples, it demonstrates how to construct robust Knowledge Graphs that serve as structured, verifiable sources of truth for LLM pipelines.Readers learn modern techniques for automated entity extraction, relationship discovery, schema population, and graph enrichment using advanced LLM prompting and hybrid NLP methods. The book outlines multiple integration patterns including Retrieval-Augmented Generation (RAG), multi-hop reasoning workflows, context fusion layers, and knowledge-guided agent architectures showing how to connect graph intelligence to model outputs.A full coverage of operational considerations is included, such as scalable graph storage, query optimization, system performance, security models, version control, and governance frameworks required for production-grade KG-LLM deployments. Detailed evaluation strategies for measuring graph quality, LLM accuracy, contextual relevance, and end-to-end pipeline performance are also provided.By bridging semantic technologies and modern AI systems, this book equips professionals to build context-aware, transparent, and highly dependable AI solutions capable of addressing hallucinations, improving explainability, and supporting critical enterprise applications. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798273753815
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Paperback. Condition: new. Paperback. This book delivers a comprehensive and implementation-focused guide to building knowledge-driven AI systems that elevate the accuracy, reliability, and interpretability of Large Language Models. Designed for machine learning engineers, data architects, AI researchers, and enterprise practitioners, it provides a complete workflow for engineering Knowledge Graphs tailored for LLM-based applications.The book begins by establishing the fundamentals of semantic data modeling, ontology development, schema design, and graph-based reasoning. Using practical examples, it demonstrates how to construct robust Knowledge Graphs that serve as structured, verifiable sources of truth for LLM pipelines.Readers learn modern techniques for automated entity extraction, relationship discovery, schema population, and graph enrichment using advanced LLM prompting and hybrid NLP methods. The book outlines multiple integration patterns including Retrieval-Augmented Generation (RAG), multi-hop reasoning workflows, context fusion layers, and knowledge-guided agent architectures showing how to connect graph intelligence to model outputs.A full coverage of operational considerations is included, such as scalable graph storage, query optimization, system performance, security models, version control, and governance frameworks required for production-grade KG-LLM deployments. Detailed evaluation strategies for measuring graph quality, LLM accuracy, contextual relevance, and end-to-end pipeline performance are also provided.By bridging semantic technologies and modern AI systems, this book equips professionals to build context-aware, transparent, and highly dependable AI solutions capable of addressing hallucinations, improving explainability, and supporting critical enterprise applications. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798273753815
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