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Knowledge Graph Data Engineering for Generative AI Use Cases

1h 50mIntermediate2025-05-30

Authors

Ashleigh Faith

Course details

This advanced course bridges the gap between traditional data engineering and modern AI applications through knowledge graphs. Designed for data scientists and engineers, instructor Ashleigh Faith provides an overview of a practical framework for implementing neurosymbolic AI solutions. Learn how to assess data requirements, build robust knowledge graphs, implement efficient ETL processes, and handle complex entity resolution challenges. Along the way, Ashleigh covers real-world applications, common pitfalls, and best practices for creating maintainable, scalable knowledge graph solutions that can integrate with AI systems.

Skills covered

Neo4jNatural Language Processing (NLP)Data VisualizationData EngineeringGenerative AIArtificial Intelligence (AI)Data ScienceBusiness Analysis and StrategyBusiness Software and ToolsOpen SourceOne-Off

Concepts

0. Introduction

01 - The power of knowledge graphs in data engineering
02 - What you need to know for the course

1. Setup - Data Engineering Foundations

03 - What is data engineering
04 - Aspects of data engineering
05 - Importance of data engineering for semantic AI
06 - Use case - Two Trees Olive Oil

2. Extraction

07 - What data do you need
08 - Where is the data
09 - What state is the data in
10 - Translating relational to graph data

3. Data Modeling

11 - Creating your design document
12 - Options for data modeling
13 - Thinking about nodes
14 - Thinking about relations
15 - Thinking about retrieval traversal
16 - Thinking about updates
17 - Thinking about storage

4. Transform

18 - Data transformation
19 - Missing data

5. Load

20 - Setting up our Stardog project, part 1
21 - Setting up our Stardog project, part 2
22 - Load instances in Stardog
23 - Test the load
24 - Test the query