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Hands-On AI: Knowledge Graphs for Generative AI Use Cases

Hands-On AI: Knowledge Graphs for Generative AI Use Cases

2h 10mAdvanced2024-09-25

Authors

Ashleigh Faith

Ashleigh Faith

Course details

Having a knowledge graph backing your LLM adds more consistency, trust, and quality to LLMs and the applications they support, and you don’t have to be an expert to begin using knowledge graphs. In this course, knowledge graph expert Ashleigh Faith covers what a knowledge graph is, the different types of knowledge graphs, and what each type is generally used for. Explore the foundations of modeling and populating a knowledge graph and architectures for supporting knowledge graphs. Learn how to query the knowledge graph and how LLMs can use knowledge graphs to support time-sensitive data and reduce hallucinations. Find out how to identify what kind of knowledge graph is best for a variety of knowledge graph use cases, as well as what architecture and tooling will best support your knowledge graph project. Plus, discover ways to construct and populate a knowledge graph and then use a knowledge graph in the LLM space.

Skills covered

Natural Language Processing (NLP)Data VisualizationGenerative AIArtificial Intelligence (AI)Data ScienceBusiness Analysis and StrategyBusiness Software and ToolsOne-Off

Concepts

0. Introduction

  • 01 - The power of knowledge graphs
  • 02 - What you should now
  • 03 - Use case introduction - Two Trees Olive Oil

1. What Is a Knowledge Graph and Why Should You Care

  • 04 - Why knowledge graphs in the LLM space
  • 05 - What is a triple or statement
  • 06 - Triple store or labeled property graph
  • 07 - What is a node or instance
  • 08 - What is an edge or relation
  • 09 - What are UIDs

2. Make Your Model

  • 10 - What to keep in mind when graph modeling for LLMs
  • 11 - How to connect nodes with relationships
  • 12 - Desktop Protege IRI setup
  • 13 - Adding instances and annotations
  • 14 - How to populate a graph
  • 15 - Adding a few constraints
  • 16 - How to update your graph
  • 17 - How to version your graph

3. Populating Your Graph with Data

  • 18 - Populating your graph model
  • 19 - What generative data can you use
  • 20 - What closed data can you use
  • 21 - What open data can you reuse
  • 22 - Attribution and sourcing
  • 23 - Checking your logic

4. GraphML

  • 24 - Queries in graph data
  • 25 - GraphML - What is it and what tools are there
  • 26 - GraphML - Walking your graph

5. Deployment

  • 27 - Data privacy, ethics, regulations, and standards
  • 28 - Automated constraint verification
  • 29 - Automated fact verification
  • 30 - Disputed fact verification
  • 31 - Entity resolution
  • 32 - Sample architecture
  • 33 - Calling your graph

Conclusion

  • 34 - Final project introduction
  • 35 - Final project walkthrough
  • 36 - Continuing on with knowledge graphs

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