Mastering Ontology Workflows For AI and Knowledge Graph Engineering: A Practical Guide to Semantic Integration, Ontology Design, Knowledge Graph Construction, AI Applications, and Real-World Projects

By: Veron Veyl

What if your data could do more than sit in disconnected databases, spreadsheets, and applications? What if you could transform scattered information into connected knowledge that powers more reliable AI systems, intelligent search, and better technical decisions?

Mastering Ontology Workflows for AI and Knowledge Graph Engineering is a practical guide to building semantic data systems that connect information, reveal meaningful relationships, and provide AI applications with structured, traceable evidence.

Whether you are an aspiring knowledge graph engineer, data professional, software developer, AI practitioner, or enterprise architect, this book takes you from foundational concepts to practical implementation.

Inside, you will learn how to:

Understand ontology engineering, semantic data, knowledge representation, RDF, RDFS, OWL, and SPARQL.Design reusable ontologies using classes, properties, axioms, domain requirements, and competency questions.Integrate data from CSV files, JSON, REST APIs, and relational databases into meaningful knowledge graphs.Apply Python, RDFLib, Prot g , and RDF stores to build reproducible semantic data workflows.Resolve entity identities, manage duplicates, preserve data provenance, and maintain consistent relationships across systems.Use OWL reasoning and SHACL validation to identify inconsistencies and improve knowledge graph quality.Build AI-powered applications using knowledge graph-enhanced retrieval, GraphRAG, vector search, embeddings, and large language models.Evaluate AI accuracy, reduce unsupported answers, trace evidence, and identify when information is insufficient.Deploy and optimize production workflows with orchestration, monitoring, security, governance, automated testing, and CI/CD.

Go Beyond Theory with Real-World Projects

Progress through practical labs, troubleshooting scenarios, and guided engineering projects, including infrastructure dependency analysis, intelligent root-cause investigation, and an end-to-end ontology-driven AI knowledge assistant.

You will learn not only how to build these systems, but also how to validate their outputs, evaluate their limitations, and maintain them responsibly.

No prior expertise in ontology engineering is required. Concepts are introduced progressively, making this guide accessible to newcomers while providing valuable implementation patterns for experienced professionals.

If you want to master ontology engineering, semantic data integration, knowledge graph construction, automated reasoning, and AI-powered knowledge systems, this book provides a practical foundation for turning disconnected data into structured, actionable knowledge.

Build the knowledge infrastructure behind more intelligent, explainable, and evidence-grounded AI applications.

• Format: Paperback
• Language: English
• ISBN: B0HPNYTDGS
• ISBN13: 9798166221643
• Release Date: October 2026
• Publisher: Independently published
• Length: 254 Pages
• Weight: 1.32 lbs.
• Dimensions: 11.0" x 0.6" x 8.5"