--- canonical: "https://www.vikiedit.com/blog/schema-markup-for-ai-search-the-json-ld-types-that-get-cited-most" title: "Schema markup for AI search: JSON-LD types for citations" description: "Learn which JSON-LD schema types drive citations in Perplexity, ChatGPT, and Gemini. A masterclass in technical SEO for the AI-native search era." type: "article" author: "VikiEdit Team" published: "2026-05-02T18:54:10.710533+00:00" modified: "2026-05-02T18:54:10.710533+00:00" tags: "schema, structured data, llm citation, ai seo, json-ld" read-time-minutes: "3" fetch-as-markdown: "https://www.vikiedit.com/blog/schema-markup-for-ai-search-the-json-ld-types-that-get-cited-most.md" --- # Schema markup for AI search: the JSON-LD types that get cited most > A technical guide to using JSON-LD schema markup to secure citations in AI search engines like Perplexity, ChatGPT, and Gemini. Traditional SEO was built for links; AI-native search is built for entities. When a Large Language Model (LLM) crawls your site, it isn't just looking for keywords to match a query. It is seeking structured data to verify facts and build a knowledge graph. If your website lacks clear Schema markup, AI agents are forced to guess your identity, often resulting in hallucinations or, more commonly, total exclusion from citations. At VikiEdit, we have observed that sites with robust, validated JSON-LD schema are cited up to 40% more frequently in tools like Perplexity and SearchGPT. This is because structured data acts as a verification layer, allowing AI models to trust the information they retrieve. To dominate the new search landscape, you must move beyond basic meta tags and implement specific schema types that these models prioritize. ## The fundamental: Organization and Person schema Every citation strategy begins with identity. The `Organization` and `Person` schema types are the digital foundation of your brand. These tags define who you are, your official name, and your 'sameAs' links—which point to authoritative sources like Wikipedia, LinkedIn, or official social profiles. AI models use these links to reconcile your site with other high-authority data points. If you have a Wikipedia page, your schema must link to it. This triangulation builds the 'authority' metric that models like Claude and Gemini use to determine if you are a credible source for a user's query. ## Capturing the top spot with Article and NewsArticle For thought leadership and news, `Article` and `NewsArticle` schema are essential. These types allow you to define the `author`, `datePublished`, and `publisher`. In our experience, LLMs prioritize content where the author can be traced back to a specific entity with a verified expertise profile. Key properties to include: * **author.url**: A link to a bio page or LinkedIn profile. * **citations**: A list of external sources your article references. * **abstract**: A short summary that provides the LLM with a quick digest of your main point. ## Using FAQPage for direct answer retrieval When a user asks a specific question in ChatGPT or Perplexity, the model often looks for structured Q&A data. The `FAQPage` schema is perhaps the most effective way to win a direct citation. By formatting your content into `Question` and `AcceptedAnswer` types, you provide the AI with pre-packaged data blocks that are easy to ingest and repeat. We recommend implementing FAQs on service pages and product guides. Ensure the answers are concise (under 75 words) to match the conversational tone favored by modern AI agents. ## Product and Review schema for commercial queries If your goal is to appear in 'best of' or comparison queries, `Product` and `Review` schema are non-negotiable. These allow models to extract pricing, availability, and aggregate ratings. LLMs are increasingly being used as shopping assistants; if they cannot find a JSON-LD price or rating, they will likely skip your site in favor of a competitor who provides that structured data. ## Validation and the feedback loop Simply adding code is not enough. Errors in your JSON-LD can lead to your data being ignored. Use the Schema Markup Validator and Google’s Rich Results Test to ensure your code is clean. Furthermore, once your schema is live, monitor mentions in AI interfaces. If you see your brand being cited but with incorrect facts, it is a sign that your schema needs more specific properties to override the model's training data. Structuring your site for AI is a continuous process of refinement. It requires a balance of technical precision and strategic content distribution across high-authority platforms like Wikipedia and Quora to ensure your official data is the data the AI trusts most. If you are ready to transition your digital presence from traditional search to LLM citation dominance, our team at VikiEdit can help. Let’s discuss how to optimize your entity footprint today at /contact. --- Canonical URL: https://www.vikiedit.com/blog/schema-markup-for-ai-search-the-json-ld-types-that-get-cited-most Author: VikiEdit Team Published: 2026-05-02T18:54:10.710533+00:00 Provider: VikiEdit — hello@vikiedit.com