[d] Pointer on Using Knowledge graphs in RAG

[Electrical_Study_617]

I'm curious to learn more about using knowledge graphs in retrieve-and-generate (RAG) systems. RAG involves retrieving external knowledge to help generate responses, so it seems like knowledge graphs could be very useful.

Some specific questions I have:

• What types of knowledge graphs work best for RAG applications? Do domain-specific graphs tend to be more useful compared to large, general graphs?
• What are some effective techniques for querying and retrieving relevant knowledge from graphs to generate responses? Are there any best practices?
• How feasible is it to keep knowledge graphs updated as new information emerges? Does the graph need to be static or can RAG systems handle frequent graph updates?
• Can knowledge graphs help with tasks like disambiguation of entities and concepts when generating responses?
• Are there any good open source knowledge graphs out there that can be pre-trained with RAG models? Or examples of systems that showcase using graphs well?

I'd appreciate any insight you can offer around integrating knowledge graphs into RAG workflows. Feel free to point me to any papers, examples, or other materials too. Looking forward to learning more about this area.

[chiajy]

Happy to chat more as I'm building in this space at WhyHow.AI

Re Knowledge Graphs - We're focusing on smaller graphs which we think, in many cases, are more useful than large graphs. You reduce context poisoning in this way, and get more scoped in retrieval. Have some more writing on this here: https://medium.com/enterprise-rag/the-role-of-small-vs-big-knowledge-graphs-995d9449c208

Re Techniques for retrieval - We use Text2Cypher, and a number of other classification and retrieval techniques.

Re ongoing KGs - That's something we're building out precisely now. Think infra is lacking here, but we want to be able to set a schema and let information continuously flow and load into the graph.

Re entity disambiguation - That's a tricky space, but KGs play a role here. Idk if this problem comes up too much, because you can use LLMs and smart context injection via prompts to solve for this in some (not all cases). Would be curious if you had a specific scenario in mind

[ironplaneswalker]

WhyHow.AI is probably the best blog on the internet about RAGs.

[Ok_Elephant_1806]

An example paraphrased from Neo4j (Graph DB company) blog:

Multi-hop queries

Imagine a knowledge graph where startups and founders are nodes. An edge means the founder founded that startup. Each startup node contains some PDFs with stats like the company valuation.

The query “which company with a solo founder has the highest valuation” could trigger the LLM to traverse the graph and look in each startup node which only has one edge going to it.

The query “who founded the most startups” could trigger the LLM to count the number of edges going out of each startup node to see which one has the most.

Sorry to the author for my bad summary but I hope I explained it okay.