Google, Microsoft, LinkedIn, and plenty of more tech corporations are using Graph RAG. Why? Let’s understand it by constructing one from scratch.
You could not understand it, but you’ve been interacting with Knowledge Graphs (KGs) more regularly than you would possibly think. They’re the technology behind many modern engines like google, Retrieval-Augmented Generation (RAG) systems for Large Language Models (LLMs), and various query tools. But what exactly are Knowledge Graphs, and why are they so integral to those technologies? Let’s delve into it.
A Knowledge Graph (KG) is a structured representation of data that captures real-world entities and the relationships between them. Imagine a network where each point represents an entity — similar to a product, person, or concept — and the lines connecting them represent the relationships they share. This interconnected web allows for a wealthy semantic understanding of knowledge, where the main target isn’t just on individual pieces of data but on how these pieces relate to 1 one other.
Nodes
At the guts of a knowledge graph are nodes (entities). As an example this, let’s consider constructing a…
