○InitializedDeepenGraphRAG & Context Retrieval

GraphRAG Brand Understanding Benchmark

Question

Does GraphRAG over a brand knowledge graph improve answer accuracy, completeness, relationship understanding and source attribution compared with a plain LLM and traditional vector RAG?

Findings

START HERE — 1) Use one fixed brand corpus and the validated Knowledge Graph. 2) Write 20 questions before the test, including factual, relationship and multi-hop questions. 3) Run the exact same 20 questions in three setups: LLM only, vector RAG, GraphRAG. 4) Keep model, temperature and question wording identical. 5) Score factual accuracy, completeness, source attribution, entity confusion and multi-hop reasoning. 6) Compare aggregate and per-question results. 7) Decide whether GraphRAG adds enough value to justify the extra complexity.

SUCCESS THRESHOLD — Deepen only if GraphRAG improves the overall benchmark by at least 10 percentage points versus vector RAG and shows a clear advantage on relationship/multi-hop questions without materially worsening source attribution.

Do not change prompts, corpus or model between configurations; otherwise the comparison is not interpretable.

Data Inputs

Brand Knowledge Graph POC outputSame 20-question benchmark for all approachesSame underlying source corpus

Next Move

If GraphRAG wins materially, test the same protocol on a second brand and define which SEO/GEO question types actually benefit from graph retrieval.

Value

★★★★★

Maturity

★★★★★

Decision

Deepen

Created

Aug 27, 2026

Updated

Aug 27, 2026

Tools Used

Semantica

Open-source graph-native infrastructure for enterprise context and accountable AI systems. Ingests structured/unstructured data, extracts entities and relationships, builds Context Graphs and Knowledge Graphs, supports ontologies, GraphRAG, graph analytics, causal reasoning, provenance and MCP exposure. GitHub: https://github.com/semantica-agi/semantica | Documentation: https://docs.getsemantica.ai/