Knowledge Graph
In one line
A knowledge graph is a database of entities — people, brands, products — and their relationships, used by search engines and LLMs as the factual backbone of their answers.
Going deeper
A knowledge graph encodes facts as nodes and edges — 'Brand X is a Korean company, makes products in category Y, founded by Z'. It is the data behind Google's Knowledge Panel, and LLMs lean on similar structures when they fact-check themselves.
What matters for marketers is whether your brand is represented correctly. If the panel has the wrong category or stale founder info, AI answers will quietly reproduce that error.
The graph reads many signals beyond your own site — Wikipedia, press, reputable directories, structured data. Working on it is really an exercise in keeping brand information consistent across the open web.
Related terms
Schema.org
Schema.org is the shared vocabulary co-sponsored by Google, Microsoft, Yahoo and Yandex that lets you label what each page means so search engines and AI can understand it.
SEOJSON-LD
JSON-LD (JSON for Linked Data) is the JSON-based format used to embed Schema.org markup in a page — currently Google's recommended way to ship structured data.
SEOE-E-A-T
E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) is Google's four-axis lens for content quality — and the same kinds of signals matter when LLMs decide whom to cite.
GEO·AEOLLMO
LLMO (Large Language Model Optimization) is the work of shaping content, data and context signals so that LLMs understand and cite your brand correctly.
GEO·AEOGEO
GEO (Generative Engine Optimization) is the practice of optimizing content and data so that a brand gets cited and recommended inside generative AI search answers like ChatGPT, Perplexity and Google AI Overviews.
How does your brand show up in AI answers?
Villion measures how your brand appears across ChatGPT, Perplexity and AI Overviews, then automates the work that lifts citation rate and share of voice.
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