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Artículo: AMZ-B0FQ6JBRPR
Knowledge Graphs for LLMs: Harnessing Structured Intelligence for Smarter AI Systems: Graph Construction, Retrieval-Augmented Generation, and Semantic ... and Workflow Systems Series Book 2)
Format:
Kindle
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En stock
En stock
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0.84 kg
0.84 kg
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No
No
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Nuevo
Nuevo
Producto de
Amazon
Amazon
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USA
USA
Sobre este producto
- 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.
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