Why Knowledge Graphs Are the Missing Layer in AI

Large language models have transformed how we interact with information — but they come with well-documented limitations. They hallucinate. They lack provenance. They struggle with structured reasoning over complex domains.

Knowledge graphs address these gaps by providing a semantic layer that grounds AI in verifiable, interconnected facts.

The Problem with Unstructured AI

LLMs excel at pattern matching across massive text corpora, but they have no inherent understanding of relationships between entities. When you ask an LLM about drug interactions, supply chain dependencies, or regulatory compliance, it generates plausible-sounding text — but cannot guarantee factual accuracy.

How Knowledge Graphs Help

A knowledge graph represents data as a network of entities and relationships, each with well-defined semantics:

• Explicit relationships: Instead of inferring connections from text, relationships are stated directly
• Provenance tracking: Every fact can be traced to its source
• Reasoning capabilities: Graph traversal enables multi-hop logical reasoning
• Schema enforcement: Ontologies ensure data consistency across the graph

GraphRAG: The Best of Both Worlds

GraphRAG combines the generative power of LLMs with the structured precision of knowledge graphs. Rather than retrieving raw text chunks, GraphRAG retrieves subgraphs — preserving the relationships and context that LLMs need to generate accurate, grounded responses.

Our benchmarks show that GraphRAG approaches consistently outperform traditional vector-based RAG on complex queries requiring multi-hop reasoning.

Getting Started

If you're building AI applications that need to be trustworthy, start by modeling your domain as a knowledge graph. Fluree makes this straightforward with native JSON-LD support and SPARQL querying.