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Artículo: AMZ-B0FWSF48M5
Knowledge Graph Engineering Handbook: Building Smarter, Context-Aware Systems with Semantic Intelligence and Graph Data Models
Format:
Kindle
Detalles del producto
Disponibilidad
Sin stock
Sin stock
Peso con empaque
0.20 kg
0.20 kg
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No
No
Condición
Nuevo
Nuevo
Producto de
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Turn scattered data into trusted, explainable intelligence. This hands-on guide shows how to design, build, and operate knowledge graphs that supercharge AI—so your models don’t just predict, they understand, prove, and improve. Written with a practitioner’s lens, the book blends industry-grade patterns (SHACL contracts, blue/green publishes, KaaS APIs) with runnable examples (RDFLib, SPARQL, Cypher, Python/pySHACL). You get rigor, not hype: clear data contracts, versioned publishes, and measurable SLOs. About the Technology You’ll learn the essentials behind RDF/OWL/SPARQL, property graphs/Cypher/Gremlin, reasoners, entity linking, graph ML (GNNs, embeddings), RAG with KGs, and neuro-symbolic loops where LLMs propose and the KG verifies. What’s InsideDesign & modeling: lifecycle, ontology/schema engineering, competency questions.Build pipeline: ingestion, normalization, entity resolution, validation, inference.Storage & query: graph databases, indexing, performance, caching.AI integration: KG-aware ML, GNNs, LLM+KG RAG, explainability with why-paths.Operations: governance, provenance, security, version control, drift monitors.Blueprints: healthcare, finance, security, search/recs, science KGs.KaaS: expose knowledge as a versioned, policy-aware service. Who this book is forML/AI engineers who need context-aware and auditable systems.Data/knowledge engineers building robust pipelines and ontologies.Product & platform teams shipping search, recommendations, assistants.Leaders/architects defining standards, governance, and SLOs for AI. LLMs without grounding risk hallucinations, fines, and lost trust. Organizations are standardizing on verifiable knowledge now—teams that move first set the data contracts and APIs everyone else must follow. Start this week: every chapter ends with quick wins—define IDs, add 5 SHACL rules, materialize 3 CONSTRUCTs, publish a blue/green graph, return a 2–5 hop explanation. Ship visible value in 30–60–90 days. One well-governed KG can power multiple products—search, recs, analytics, and copilots—reducing rework, lowering risk, and increasing trust. The book’s patterns are tool-agnostic, so your investment compounds across stacks. Build AI people can trust. Pick up Knowledge Graph Engineering Handbook, adopt the templates, and launch your first verifiable, explainable KG-powered feature this quarter. Your data already knows the answers—let’s make your AI prove them.
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