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Knowledge Graph Engineering Handbook: Building Smarter, Context-Aware Systems with Semantic Intelligence and Graph Data Models

by Brayden Ernest
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₹3,134.00
Original price ₹3,134.00
Original price ₹3,134.00
₹3,134.00
Current price ₹3,134.00

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Book cover type: Paperback
  • ISBN13: 9798270394578
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Publication Date:
  • Pages: 514
  • Original Price: USD 29.99
  • Language: English
  • Edition: N/A
  • Item Weight: 808 grams
  • BISAC Subject(s): Artificial Intelligence / Natural Language Processing

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 Inside

  • Design & 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 for
  • ML/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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