Structured Data
구조화 데이터
A machine-readable metadata format that uses Schema.org vocabulary to describe the meaning of page content.
Structured-data markup that uses the Schema.org vocabulary to describe content and entities semantically. Review implementation considerations.
스키마 마크업
Schema markup uses a shared vocabulary containing hundreds of types, including Article, Product and FAQPage, and their associated properties. The vocabulary evolves through new releases, so monitor changes relevant to your content rather than assuming every new property is AI-specific or supported as a search enhancement. Reusable page-type templates mapped automatically to CMS fields provide an efficient maintenance model.
Provides a technical foundation for machine-readable content relationships used by search and other systems.
Monitor vocabulary updates and remove obsolete or unnecessary implementation details where appropriate.
<!-- 스키마 마크업의 가장 흔한 형태: Article 예시 -->
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "Article",
"headline": "스키마 마크업 실전 가이드",
"author": { "@type": "Person", "name": "홍길동" },
"datePublished": "2026-01-15"
}
</script>
구조화 데이터
A machine-readable metadata format that uses Schema.org vocabulary to describe the meaning of page content.
리치 결과
An enhanced search result with additional elements such as ratings, prices, images or supported expandable information.
AI 검색
Search that uses large language models to understand context and synthesize web results into natural-language answers rather than relying solely on keyword matching.
FAQ 구조화 데이터
Markup that describes a set of frequently asked questions and answers, eligible in supported cases for expanded search presentations.
Google Discover
Google's personalized content feed, which recommends material based on interests and activity without requiring a search query.
JSON-LD 구조화 데이터
A structured-data format that expresses linked data as JSON in a script element, commonly used in an HTML head.