Hey everyone! Thank you so much for watching the second episode of AI-Native Databases with Paul Groth! This was another epic one, diving deep into the role of structure in our data! Beginning with Knowledge Graphs and LLMs, there are two perspectives: LLMs for Knowledge Graphs (using LLMs to extract relationships or predict missing links) and then Knowledge Graph for LLMs (to provide factual information in RAG). There is another intersection that sits in the middle of both LLMs for KGs and KGs for LLMs, which is using LLMs to query Knowledge Graphs, e.g. Text-to-Cypher/SPARQL/... From there I think the conversation evolves in a really fascinating way exploring the ability to structure data on-the-fly. Paul says "Unstructured data is now becoming a peer to structured data"! I think in addition to RAG, Generative Search is another underrated use case -- where we use LLMs to summarize search results or parse out the structure. Super interesting ideas, I hope you enjoy the podcast -- as always more than happy to answer any questions or discuss any ideas you have about the content in the podcast!

Learn more about Professor Groth's research here: https://scholar.google.com/citations?...

Knowledge Engineering using Large Language Models: https://arxiv.org/pdf/2310.00637.pdf

How Much Knowledge Can You Pack into the Parameters of a Language Model? https://arxiv.org/abs/2002.08910

Chapters

0:00 AI-Native Databases!
0:58 Welcome Paul!
1:25 Bob’s overview of the series
2:30 How do we build great datasets?
4:28 Defining Knowledge Graphs
7:15 LLM as a Knowledge Graph
15:18 Adding CRUD Support to Models
28:10 Database of Model Weights
32:50 Structuring Data On-the-Fly

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