Generative Engine Optimization (GEO) & Knowledge Graph SEO: What SPARQL Data Shows for US Local Businesses (2026)

If you are evaluating generative engine optimization (GEO)—including GEO analysis software, GEO tracking software, or a full generative engine optimization platform—one practical question is whether your industry is even represented in the public knowledge graphs that power answers in ChatGPT, Claude, and Perplexity. Traditional SEO optimizes pages; GEO and knowledge graph SEO address whether your business exists as a structured entity that large language models (LLMs) can ground on. This post gives a data-backed snapshot using SPARQL queries against Wikidata, so you can pair search-friendly terms like knowledge graph LLM and GEO platform comparison with real numbers—not anecdotes.

Data snapshot: Wikidata Query Service, aggregated 2026-03-30 (see methodology). Counts are written to reports/wikidata-multi-industry-coverage.json by our multi-industry coverage script (scripts/wikidata-multi-industry-coverage-stats.ts).

Why “knowledge graph” shows up next to “LLM” and “GEO”

• LLMs do not browse your site like a user; they often rely on structured entity data (Wikidata, Wikipedia, and related graphs) when answering “who is near me” or “which firm should I call.”
• Generative engine optimization is the practice of improving how your brand appears in those AI-generated answers—overlapping with AI search visibility and local SEO, but focused on entities and citations, not only blue-link rankings.
• Knowledge graph SEO is the work of getting accurate, complete business entities into public graphs (and keeping them maintained), which supports both GEO tooling and long-term trust in AI-facing answers.

So when people search for knowledge graphs and LLMs or LLM knowledge graph, they are often asking: Is my business in the graph the model uses? SPARQL counts help answer that at the industry level.

US local businesses in Wikidata (official website required)

These figures count United States entities (P17 = United States) with an official website (P856), using industry definitions that match our agency reporting (see methodology). US hospitals are included as a reference point for how skewed coverage can be toward large institutions.

Plain-language readout:

• Law firms have the largest in-graph footprint among these three verticals (327 US firms with a site in Wikidata)—still a small slice of the real-world market, but materially more than clinics or real estate in this snapshot.
• Medical clinics remain the steepest gap relative to hospitals: 3,331 US hospitals appear in Wikidata versus 38 US clinics under our clinic definition. For generative AI optimization in healthcare (including “generative AI optimization for lawyers”-style positioning in other verticals), the story is similar: institutions are easier to find in the graph than typical SMB practices.
• Real estate companies sit between law and clinics (54 US entities with a website), which matters for local business SEO teams pitching AI visibility alongside maps and reviews.

Use this table in GEO platform comparison conversations: buyers can see which verticals are under-filled in Wikidata and why GEO software that includes knowledge graph publishing is not interchangeable with rank trackers alone.

What this implies for GEO analysis vs. GEO tracking

• GEO analysis software should surface entity-level gaps (missing or thin Wikidata records, weak connections to locations and industries), not only prompt-level mentions.
• GEO tracking software should monitor AI assistant answers over time; without knowledge graph investment, improvements may hit a ceiling if the business is absent from the underlying data sources assistants use.

Together, they match how teams actually buy: some need a one-time audit and remediation; others need ongoing monitoring—see our GEO platform comparison for a feature checklist.

Methodology (reproducible SPARQL)

All numbers come from the public Wikidata Query Service. Definitions align with wikidata-multi-industry-coverage-stats.ts:

• US: wdt:P17 wd:Q30
• Website: wdt:P856 present
• Law firm: wdt:P31 wd:Q613142
• Real estate company: wdt:P31 wd:Q1660104
• Medical clinic: health care facility (Q1774898) or business (Q4830453) with medical specialty (P1995), excluding hospitals (Q16917)
• US hospitals (comparison): wdt:P31 wd:Q16917 with P17 Q30

Example pattern (law firms with website in the US)—you can paste into the query UI:

SELECT (COUNT(DISTINCT ?item) AS ?count) WHERE {
  ?item wdt:P31 wd:Q613142 .
  ?item wdt:P856 [] .
  ?item wdt:P17 wd:Q30 .
}

To refresh counts locally, run:

npx tsx scripts/wikidata-multi-industry-coverage-stats.ts

Related reading and next steps

• Industry framing: Which US Industries Have the Biggest Knowledge Graph Gap? (2026) (uses the same report; narrative focus differs).
• Agency positioning: Wikidata Local Business Coverage: What SEO Agencies Need to Know (2026).
• AI visibility for SEO agencies — implementation paths for knowledge graph publishing and AI visibility monitoring.

Internal links

• GEO Platform Comparison 2026
• Knowledge Graph Publishing for AI Visibility
• Schema Markup & AI Search Visibility
• Engineering Knowledge Graphs for LLM Semantics
• For Agencies