{ "item_title" : "Knowledge Graphs for LLMs", "item_author" : [" Zhao Miller "], "item_description" : "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 thisData 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.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/9/79/826/445/9798264455261_b.jpg", "price_data" : { "retail_price" : "19.80", "online_price" : "19.80", "our_price" : "19.80", "club_price" : "19.80", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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