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Knowledge Graph Engineering Handbook: Building Smarter, Context-Aware Systems with Semantic Intelligence and Graph Data Models
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Publisher: Independently Published
Published: Oct 17 2025
Pages: 514
Weight: 1.78
Height: 1.03 Width: 6.69 Depth: 9.61
Language: English
- 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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