The structured representation of entities (brands, products, people, places) and their relationships that AI engines use to understand and cite your brand.
Knowledge graphs are structured data networks (entities + relationships) that power AI engine understanding. Major AI engines maintain internal knowledge graphs based on Wikipedia, Wikidata, Google Knowledge Graph, and crawled web data. Brands with well-defined entities in these knowledge graphs (clear company description, leadership, founding date, product list, competitive set) are more likely to be cited accurately. Schema.org markup, llms.txt, and Wikipedia presence all contribute to knowledge graph clarity.
AI engines pull factual claims from knowledge graphs. Brands with weak knowledge graph presence (no Wikipedia article, sparse Schema.org markup, inconsistent third-party descriptions) get less accurate citations. Knowledge graph optimization is foundational AEO infrastructure.
A B2B SaaS company with a Wikipedia article, complete Wikidata entry, Schema.org/Organization markup on their site, and consistent descriptions across Crunchbase, AngelList, and ProductHunt has a strong knowledge graph presence. AI engines cite them with accurate facts. A competitor without Wikipedia presence and inconsistent descriptions gets cited less reliably and sometimes inaccurately.
The terms in this glossary aren't theoretical — they're what Lantern's product calculates and reports every month for B2B SaaS teams. See yours in 7 days. 14-day free trial.
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