Course outcomes:
- Understand what a knowledge graph is and how it can benefit your data science applications when paired with Large Language Models (LLMs)
- Learn how to set up a knowledge graph development project
- Understand the project steps, techniques, and technologies that can support them, and the factors that are critical for success
- Learn how to use knowledge graphs to make LLMs more accurate and reliable
Join expert Panos Alexopoulos in an engaging course that explores knowledge graphs and their partnership with Large Language Models (LLMs). You’ll learn the main stages of knowledge graph development, including kickstarting the project, developing the schema, populating the graph, and controlling its quality. You’ll understand the skills necessary for building real-world knowledge graphs, such as acquiring requirements from stakeholders, designing schemas in Neo4j, discovering entities and relations from text with NLP tools, and detecting quality issues. You will learn how LLMs can effectively help you in building your knowledge graph but also how your knowledge graph can enhance the accuracy of LLMs and elevate your data science capabilities. The first week focuses on knowledge graph basics, including how to create and manipulate data in Neo4j, while the second week’s focus is populating and controlling the quality of the knowledge graph, with practical exercises in mining entities and relations, detecting knowledge graph quality problems, and using knowledge graphs to eliminate LLM hallucinations and improve their accuracy, reliability and explainability.
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