How to Build a Knowledge Graph in Minutes

I tried and failed creating one—but it was when LLMs were not a thing!

At first, knowledge graphs (KGs) sounded daunting—not the concept, but the process of constructing one.

I’ve tried constructing a knowledge graph before and failed.

Graphs are undoubtedly one of the best ways to represent complex relationships. They have many uses, such as recommendation systems and fraud detection. However, the one that caught my interest the most was information retrieval.

I started using knowledge graphs to build better RAGs.

RAGs don’t necessarily demand knowledge graphs. They don’t demand a database at all. As long as you can extract relevant information from a large pool and pass it to the context of an LLM, RAGs work.

You could build a RAG with a web search as its information retrieval strategy or use a vector store to benefit from its semantic text search features.

If you use a graph database to retrieve contextual information, we call it GraphRAG.

This isn’t a post about GraphRAGs (Perhaps in a future post). This is about the construction of knowledge graphs itself using LLMs. But it’s worth mentioning…