COMPARISON MATRIX
RAG vs. Knowledge Graphs
Comparing probabilistic semantic vector lookup against deterministic entity-relationship graph reasoning.
Option A
Retrieval-Augmented Generation (RAG)
Option B
Knowledge Graphs
| Capability / Dimension | Retrieval-Augmented Generation (RAG) | Knowledge Graphs |
|---|---|---|
| Data Representation | Unstructured text chunks converted into high-dimensional vectors | Structured nodes, edges, properties, and ontologies |
| Query Mechanism | Approximate Nearest Neighbor (ANN) vector similarity (Cosine, Dot) | Graph traversals, Cypher/SPARQL queries, multi-hop joins |
| Reasoning Capability | Single-passage semantic matching; struggles with multi-hop relationships | Explicit multi-hop relationship reasoning and community summaries |
| Determinism & Accuracy | Probabilistic; subject to retrieval noise and hallucination | Deterministic factual relationships; schema-bound precision |
| Setup Complexity | Low; chunk text, embed, and store in vector DB | High; requires ontology modeling and entity extraction pipelines |
| Best Use Case | Standard document Q&A and semantic passage retrieval | Complex domain analytics, enterprise compliance, and multi-hop queries |