Knowledge Graphs for LLMs: Harnessing Structured Intelligence for Smarter AI Systems: Graph Construction, Retrieval-Augmented Generation, and Semantic Workflows - Softcover
Synopsis
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 inside
- Fundamentals 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.
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 & format
- Reproducible projects that convert raw text into production-ready graph assets.
- Query recipes, integration blueprints, and operational guidelines for graph maintenance and scaling.
"synopsis" may belong to another edition of this title.
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Paperback. Condition: new. Paperback. 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. 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 # 9798264455261
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Paperback. Condition: new. Paperback. 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. 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 # 9798264455261
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