How knowledge graphs work and why they are the key to context for enterprise AI

This post details the technical underpinnings of Glean's knowledge graph, explaining its structure (entities, relationships, triplets, edge properties), disambiguation processes for entities and intents, and how it grounds LLMs for more reliable enterprise AI. It highlights the limitations of LLMs in handling multi-hop reasoning, enterprise-specific language, and deterministic queries, contrasting them with the structured, relationship-based approach of knowledge graphs. The post also introduces Glean's 'personal graph' as an extension to better understand human context at work.