Schema markup translates your website's language into AI's language. HTML body alone doesn't fully convey *subject, relationships, and attributes* to search engines and LLMs. A meta-layer structured with Schema.org vocabulary — JSON-LD, Knowledge Graph connection, rich results, AI citation candidate signals — all in one curated guide.
Three core Schema Markup terms
Markup that uses the vocabulary defined by Schema.org to give HTML content machine-readable meaning. A meta-layer that helps search engines and LLMs accurately interpret a content's subject, relationships, and attributes.
Google's recommended structured data format. Separated from HTML body and placed inside <script type="application/ld+json">, offering the best maintainability and readability.
Google's entity-based fact database. Entities are registered through schema markup plus external authority sources (Wikipedia, LinkedIn, press) — the core foundation for AI to recognize a company as an *answer candidate*.
Schema essence · integrated architecture design · Knowledge Graph connection · citation conditions — the role Schema Markup plays in GEO, explored in depth. Original articles are in Korean.
3 conditions AI search engines use when selecting citation sources. How to design structured data to raise citation priority.
READ MORE (KR)The essence of the Schema.org standard and JSON-LD application principles. Schema's role in rich results and AI citation candidate signals — from a definitional perspective.
READ MORE (KR)The synergy that emerges when schema, semantics, content, and entity design are bound into one architecture. Step-by-step renewal principles.
READ MORE (KR)How Google's Knowledge Graph recognizes entities and the mechanism by which schema markup combines with external authority sources.
READ MORE (KR)Google states schema is not a direct ranking factor. However, it contributes to (1) rich result eligibility (stars, FAQ, HowTo, events), (2) Knowledge Graph entity connection, (3) AI search citation candidate signals. Not a ranking signal, but a meta-layer that creates visibility and citation opportunities.
Google officially recommends JSON-LD. It separates from HTML body for better maintainability and can cleanly group multiple entities (@graph) on a single page. Microdata is only recommended for legacy site compatibility; RDFa is for special cases like academic sites.
(1) Organization·LocalBusiness — company entity registration, (2) WebSite + SearchAction — Sitelinks search box, (3) BreadcrumbList — site structure display, (4) Article·BlogPosting — content pages, (5) FAQPage — FAQ rich results, (6) Service — service pages, (7) Product — product pages.
(1) Google Search Console's Enhancements report — applied schema types and errors, (2) Rich Results Test tool — per-URL validation, (3) monitor rich snippet appearance in actual search results, (4) check whether the company appears as a citation source in AI search via reproducible queries.
(1) Whether deliverable spec lists schema types and JSON-LD code, (2) whether Search Console validation screenshots are provided, (3) whether Rich Results Test passes, (4) @graph structure and @id linking — whether the entity graph connects consistently. Plugin solutions often apply only basic schema, insufficient for GEO.
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