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

By Brayden Ernest

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.

• Format: Paperback
• Language: English
• ISBN: B0FWWR27B4
• ISBN13: 9798270394578
• Release Date: October 2025
• Publisher: Independently Published
• Length: 514 Pages
• Weight: 1.78 lbs.
• Dimensions: 1.0" x 6.7" x 9.6"