Production Knowledge Graph Engineering: A Hands-On Guide to Python, Neo4j, RDF, OWL, SPARQL, GraphRAG, LLMs, and Enterprise AI

Your AI is only as reliable as the knowledge behind it.

Organizations have more data than ever—spread across databases, APIs, documents, data lakes, vector stores, and disconnected applications. But when you need answers across that information, traditional queries, simple search, and vector similarity quickly reach their limits.

If you're a software engineer, data engineer, AI/ML engineer, knowledge engineer, or technical architect, you've probably faced the same challenge: How do you transform fragmented data into a connected, trustworthy knowledge graph that applications and AI systems can actually use?

And if you're trying to master knowledge graph engineering, the learning path can feel just as fragmented. One resource teaches RDF and the semantic web. Another focuses on Neo4j as a graph database. Another explores ontology engineering, while newer resources jump directly into GraphRAG and LLMs—without showing how entity resolution, semantics, provenance, retrieval, and production controls fit together.

Production Knowledge Graph Engineering changes that.

This hands-on guide shows you how to build an evolving Production Knowledge Graph Platform from the ground up—combining semantic standards, property graphs, data engineering, hybrid retrieval, GraphRAG, and enterprise AI within one coherent system.

Inside, you'll learn how to:

• Design production knowledge graphs with stable entities, relationships, canonical identity, and provenance

• Build graph applications with Python, Neo4j, and Cypher

• Engineer semantic knowledge with RDF, RDFLib, RDFS, OWL, SPARQL, and SHACL

• Apply semantic web standards and practical ontology engineering

• Design Neo4j graph database schemas, constraints, indexes, transactions, and traversals

• Build ingestion pipelines and perform entity resolution across heterogeneous data sources

• Generate graph-linked embeddings and implement vector search

• Combine Cypher, SPARQL, vector search, and graph traversal for hybrid retrieval

• Build an end-to-end GraphRAG pipeline with Python and Neo4j

• Ground LLMs in connected, traceable evidence instead of similarity alone

• Generate safer Cypher and SPARQL from natural-language questions

• Build enterprise search, knowledge assistants, APIs, and knowledge-aware AI agents

• Apply security, governance, testing, evaluation, observability, versioning, and CI/CD

• Deploy, scale, back up, recover, and operate knowledge graph systems in production

This isn't a collection of disconnected tutorials. You'll progressively engineer the same platform from raw enterprise data to governed knowledge, graph and vector retrieval, GraphRAG, enterprise AI, and production operations.

Whether you're entering knowledge graph engineering or adding graph intelligence to modern AI systems, you'll gain more than syntax. You'll learn how to turn fragmented data into dependable knowledge infrastructure—and connect semantic technologies, graph databases, retrieval, and generative AI without sacrificing trust or production reliability.

Stop treating knowledge graphs, vector search, and LLMs as separate systems. Build the knowledge layer that brings them together.

Start building production-ready knowledge graphs and grounded enterprise AI today.