Knowledge Graphs

Structured representations of entities and relationships - the foundation for GraphRAG, enterprise search, and neuro-symbolic AI.

Understand what knowledge graphs are, how they represent real-world entities and relationships, and why they matter for AI.

Labeled property graph model - nodes and edges with key-value properties, used by Neo4j and most graph databases.

The declarative query language for property graphs - pattern matching, traversals, and Neo4j queries.

Stardog, Neo4j, Amazon Neptune, TigerGraph, and RDF stores - choosing and operating graph databases in production.

Compare the two dominant knowledge graph models - RDF (W3C standard) and property graphs (Neo4j-style) - and when to use each.

Learn about the Resource Description Framework - the standard for representing knowledge as linked data.

Master the query language for RDF knowledge graphs and linked data endpoints.

Understand how ontologies define the vocabulary, relationships, and rules for a domain of knowledge.

Learn how SHACL (Shapes Constraint Language) validates and constrains RDF data against defined shapes.

Explore the Web Ontology Language for defining rich, machine-interpretable ontologies with reasoning capabilities.

Learn how organizations use knowledge graphs to connect data silos and power intelligent applications.

Explore how knowledge graphs enhance RAG pipelines with structured relationships and reasoning.

Discover how knowledge graphs and large language models complement each other - grounding LLMs with structured knowledge.

Practical guidelines for designing, building, and maintaining knowledge graphs in production.