Knowledge Graphs for LLMs: Harnessing Structured Intelligence for Smarter AI Systems: Graph Construction, Retrieval-Augmented Generation, and Semantic

By Zhao Miller

A professional, applied manual for designing, building, and operationalizing knowledge graphs that materially improve LLM-driven systems. This book provides the end-to-end technical roadmap data ingestion, entity/relation extraction, schema design, storage, querying, and LLM integration required to create explainable, high-precision hybrid AI systems.
What's insideFundamentals of graph modeling, schema & ontology design, and graph theory essentials.Practical pipelines for extracting structured facts from unstructured text using NLP and embeddings.Integration patterns for Neo4j/RDF/graph stores, vector databases, and RAG architectures.Querying and analytics: SPARQL, Cypher, and hybrid retrieval approaches.Performance optimization, versioning, governance, and visualization techniques.Domain case studies (healthcare, finance, enterprise search) demonstrating measurable ROI.Key topics;
knowledge graphs, graph databases, ontology design, entity extraction, SPARQL, Cypher, RAG, embeddings, semantic search, graph-augmented LLMs, information retrieval, data governance.
Who should read this
Data engineers, knowledge engineers, ML/AI practitioners, and technical product managers tasked with building authoritative retrieval systems or explainable AI features. A working knowledge of databases and basic NLP is helpful.
Deliverables & formatReproducible projects that convert raw text into production-ready graph assets.Query recipes, integration blueprints, and operational guidelines for graph maintenance and scaling.

• Format: Paperback
• Language: English
• ISBN: B0FQJNDN7Y
• ISBN13: 9798264455261
• Release Date: September 2025
• Publisher: Independently Published
• Length: 196 Pages
• Weight: 0.77 lbs.
• Dimensions: 0.4" x 7.0" x 10.0"