(no slide links)

(no transcription)

Paco Nathan  
2024-08-12 12:34:00

What's a good way to convert text documents into a knowledge graph? According to the current open source libraries for GraphRAG, a dominant notion is: "Just use an LLM to generate a graph automatically, which should be good enough to use." For those of us who work with graphs in regulated environments or mission-critical apps, obviously this isn't appropriate. In some contexts it may even represent unlawful practices, e.g., given US laws regarding data management in some federal agencies. Let's step back to review the broader practices in knowledge graph construction. For downstream use cases, such as where KGs are grounding AI apps, there's larger question to ask. How can we build KGs from both structured and unstructured data sources, and keep human expert reviews in the loop, while taking advantage of deep learning and open models? This talk provides a step-by-step guide to working with unstructured data sources for constructing and updating knowledge graphs. We'll assume you have some experience coding in Python and working with popular open source tools.