Lilly Tech Systems
Enterprise Knowledge Graphs
Knowledge graphs capture entities and relationships as structured, queryable networks. Learn graph modeling, Neo4j, construction techniques, and how knowledge graphs power enterprise AI applications from RAG to recommendations.
Your Learning Path
Follow these lessons to master enterprise knowledge graphs for AI.
1. Introduction
What are knowledge graphs, why they matter for enterprise AI, and key concepts.
2. Graph Modeling
Nodes, edges, properties, ontologies, and schema design for enterprise knowledge.
3. Neo4j
Cypher query language, data modeling in Neo4j, and building your first knowledge graph.
4. Construction
Building knowledge graphs from structured data, text, and using LLMs for extraction.
5. Applications
GraphRAG, recommendations, fraud detection, drug discovery, and enterprise search.
6. Best Practices
Scaling, maintenance, quality assurance, and organizational patterns for knowledge graphs.
What You'll Learn
By the end of this course, you'll be able to:
Model Knowledge
Design graph schemas and ontologies that capture enterprise domain knowledge.
Build with Neo4j
Create, query, and manage knowledge graphs using Neo4j and Cypher.
Construct Graphs
Extract entities and relationships from structured data, text, and using LLMs.
Power AI Applications
Use knowledge graphs for GraphRAG, recommendations, and intelligent search.