Overview

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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