Production Ontology Engineering: A Hands-On Guide to Python, RDF, OWL, SPARQL, SHACL, Knowledge Graphs, GraphRAG, LLMs, and AI Agents

Your organization has more data than ever-but does that data actually mean the same thing across databases, APIs, documents, knowledge graphs, and AI systems?Modern systems rarely fail because they lack information. They fail because information is fragmented across incompatible schemas, inconsistent terminology, duplicate identities, and disconnected applications. Add LLMs and AI agents, and the challenge grows: fluent answers are useless when the underlying knowledge cannot be identified, validated, traced, or trusted.If you're a knowledge engineer, ontology engineer, data engineer, software engineer, AI/ML engineer, or data architect, you need more than another introduction to the semantic web. You need to know how ontology engineering, semantic data modeling, and knowledge representation work in real production systems.Production Ontology Engineering takes you from foundational ontology development to building and operating a complete semantic knowledge platform. Using Python and an evolving Production Enterprise Knowledge Platform, you'll learn how RDF, RDFS, OWL, SKOS, SPARQL, and SHACL work together-and how to carry those technologies into knowledge graph engineering, GraphRAG, LLM knowledge graph systems, and ontology-grounded AI agents.Inside, you'll learn how to: - Design production-ready ontologies with classes, properties, axioms, competency questions, IRIs, namespaces, taxonomies, and controlled vocabularies- Build semantic web systems with Python, RDFLib, OWLReady2, and Protégé- Query and transform knowledge graphs with SPARQL and build semantic APIs- Validate semantic data with SHACL and pySHACL and automate quality and regression testing- Engineer ontology workflows with Git, CI/CD, versioning, reproducible releases, and Docker- Build an enterprise knowledge graph from databases, CSV, JSON, APIs, and documents while handling identity, provenance, temporal knowledge, and trust- Combine embeddings, entity linking, semantic search, vector retrieval, and graph traversal for GraphRAG and hybrid retrieval- Build an AI knowledge graph foundation for grounded LLM applications and knowledge-aware agents- Evaluate retrieval quality, grounding, hallucination, faithfulness, and evidence sufficiency- Operate semantic infrastructure with governance, security, observability, semantic drift detection, SLOs, scaling, and recovery This isn't a collection of disconnected RDF or OWL tutorials. You'll build one evolving enterprise platform in which the ontologies, knowledge graphs, validation rules, queries, and mappings created early become the foundation for the GraphRAG, LLM, and AI agent systems built later.Stop treating ontologies as static modeling files. Start engineering them as production infrastructure for connected data, enterprise knowledge, and trustworthy AI.Get Production Ontology Engineering by Lucan Verid and start building knowledge and AI systems that can actually depend on their data's meaning.