Structured Data & Semantic Web

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.

Business applications and impact#

Provides a technical foundation for machine-readable content relationships used by search and other systems.

Implementation guidance and considerations#

Monitor vocabulary updates and remove obsolete or unnecessary implementation details where appropriate.

Code example#

<!-- 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>

Structured Data

구조화 데이터

A machine-readable metadata format that uses Schema.org vocabulary to describe the meaning of page content.

Rich Results

리치 결과

An enhanced search result with additional elements such as ratings, prices, images or supported expandable information.

AI Search

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.

FAQPage Schema

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 Discover

Google's personalized content feed, which recommends material based on interests and activity without requiring a search query.

JSON-LD

JSON-LD 구조화 데이터

A structured-data format that expresses linked data as JSON in a script element, commonly used in an HTML head.