While retrieval-augmented generation is effective for simpler queries, advanced reasoning questions require deeper connections between information that exist across documents. They require a knowledge graph.
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How does the journey to a knowledge graph start with unstructured data—such as text, images, and other media? The evolution of web search engines offers an instructive example, showing how knowledge can be extracted from unstructured sources and refined over time into a structured, interconnected graph. As we will show in this post, this process underpins the journey from retrieval-augmented generation (RAG) systems to more sophisticated approaches like GraphRAG and Knowledge-GraphRAG.
From isolated nodes to graph of knowledge and knowledge graph
Early search engines like AltaVista relied on simple keyword matching, treating web pages as isolated entities. However, web pages are interconnected through hyperlinks. Google transformed search by recognizing that the world wide web is not merely a collection of standalone pages but a vast network of …