Foundations of Knowledge Graphs for LLMs: Knowledge Representation, Graph Construction, and Context-Aware Language Models

By Lin Prescott

Large language models are powerful but they do not inherently possess a reliable, structured representation of the world. They can struggle with factual consistency, complex relationships, long-context retrieval, and explaining where their answers come from.

Foundations of Knowledge Graphs for LLMs provides a practical introduction to designing and building Knowledge Graph systems that give LLM applications structured, connected, and meaningful context.

Rather than treating Knowledge Graphs as an isolated database technology, this book explores how graphs can become an essential component of modern AI architectures.

You will learn how to:

Understand the core concepts behind Knowledge Graphs and knowledge representationDesign graph schemas, entities, relationships, properties, and ontologiesWork with structured and unstructured data as sources for graph constructionExtract entities and relationships from documents using LLMs and modern NLP techniquesBuild Knowledge Graph construction pipelinesIntegrate Knowledge Graphs with Large Language ModelsConnect graph retrieval with Retrieval-Augmented Generation architecturesImprove contextual grounding and reduce unsupported LLM responsesImplement graph-based retrieval and context assembly strategiesDesign systems capable of following relationships across connected informationUnderstand graph databases, embeddings, vector search, and hybrid retrievalBuild practical Python-based Knowledge Graph and LLM workflowsDesign architectures that can evolve from prototypes into scalable AI systems

The book combines foundational concepts with practical implementation patterns, helping readers understand not only what Knowledge Graphs are, but also how and why they fit into modern LLM applications.

Whether you are new to Knowledge Graphs or already working with LLMs and RAG systems, this volume provides the foundation needed to design context-aware AI applications built around structured knowledge.

Build the foundation. Structure the knowledge. Give your LLMs better context.

• Format: Paperback
• Language: English
• ISBN: B0HDN4Z1F2
• ISBN13: 9798191849430
• Release Date: August 2026
• Publisher: Independently published
• Length: 354 Pages
• Weight: 1.36 lbs.
• Dimensions: 10.0" x 0.8" x 7.0"