○InitializedDeepenKnowledge Graphs & Semantic Representation

Brand Knowledge Graph POC

Question

Can Semantica reconstruct a useful, coherent and source-grounded knowledge graph of a brand from a limited website corpus, and reveal missing, ambiguous or weak entity relationships relevant to GEO?

Findings

START HERE — 1) Choose 1 relatively simple brand/site. 2) Select 20–50 pages covering products/services, About, FAQ, expertise, locations and certifications where relevant. 3) Ingest the corpus into Semantica. 4) Generate entities, relations and the graph. 5) Manually review at least 30 important entities and 30 important relations against the website. 6) Record missing entities, wrong relations, ambiguous concepts and unsupported claims. 7) Decide whether the graph reveals actionable GEO insights.

SUCCESS THRESHOLD — Continue if ≥85% of reviewed entities and ≥80% of reviewed relations are correct, provenance is usable, and the graph surfaces at least 3 meaningful semantic/GEO issues not obvious from a classic page-level review.

Do not interpret a correct Knowledge Graph as proof of improved visibility in Google or LLMs; this test evaluates representation quality only.

Data Inputs

20–50 representative website pagesAbout/company pagesProduct and service pagesFAQLocationsPeople or expertise pagesCertifications and authoritative claims

Next Move

If successful, use the validated graph as the corpus for the GraphRAG Brand Understanding Benchmark.

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/