Glossary of AI Search and Schema Terms

A

AI Overviews AI Search: Google's AI-generated summary that appears above traditional search results for many queries. It pulls information from multiple sources and synthesises an answer, often citing the pages it drew from. Getting cited in AI Overviews requires your content to be clearly structured, factually credible, and marked up with schema where applicable.
AI Search AI Search: Search engines and tools that use large language models to generate answers rather than simply returning a list of links. Examples include Google AI Overviews, ChatGPT search, Perplexity, and Bing Copilot. AI search systems retrieve candidate content from the web, assess its authority and structure, then synthesise an answer and select sources to cite.
AI Visibility AI Search: The degree to which a business or website is surfaced, cited, or recommended by AI search systems. Unlike traditional SEO -- which measures rankings and clicks -- AI visibility measures whether AI tools present your business as a credible answer to relevant queries. It depends on structured data, content quality, entity recognition, and authority signals.
Article Schema Schema: A type of schema markup applied to blog posts, news articles, and editorial content. It tells AI systems what a piece of content is about, who wrote it, when it was published and last updated, and what organisation published it. The dateModified field is particularly important -- AI systems use it to assess whether content is current.
Authority Signals AI Search: The combination of factors that lead an AI system to trust and cite a source. These include inbound links from credible sites, consistent entity information across the web, author credentials, structured data, publication history, and third-party mentions. Authority signals are the AI-era equivalent of domain authority in traditional SEO.

C

ChatGPT Search AI Search: OpenAI's web-connected version of ChatGPT, which retrieves live content from the internet to answer queries. When it cites a source, that citation can drive meaningful referral traffic. It tends to favour sources with clear entity signals, well-structured content, and consistent information across multiple web properties.
Citation AI Search: When an AI search system references your content as a source in its generated answer. A citation is the primary goal of AI visibility work -- it means an AI has assessed your content as credible and relevant enough to show to its users. Citations typically include a link and a short excerpt, driving both traffic and brand recognition.
Crawlability AI Search: How easily search engine and AI bots can access, read, and index your pages. If your content is blocked by robots.txt, hidden behind JavaScript that bots cannot execute, or locked behind a login, AI systems cannot assess it and will not cite it. Good crawlability is the foundation everything else depends on.

E

E-E-A-T AI Search: Experience, Expertise, Authoritativeness, and Trustworthiness -- the four quality dimensions Google uses to assess content. Experience refers to first-hand knowledge of the subject. Expertise means demonstrated depth. Authoritativeness means being recognised by others in the field. Trust is the overall credibility of the site and its content. AI search systems use similar criteria when selecting sources to cite.
Entity AI Search: A distinct, identifiable thing in the real world -- a person, business, place, product, or concept -- that AI systems can recognise and reason about. Entities are the building blocks of knowledge graphs. When an AI system can confidently identify your business as a known entity, it can draw on information about you from multiple sources and is more likely to include you in relevant answers.
Entity SEO AI Search: The practice of structuring your online presence so that AI systems and search engines can confidently identify your business as a distinct entity. This involves consistent NAP data, schema markup, Knowledge Graph entries, Wikipedia or Wikidata presence, and having your entity referenced by credible third-party sources.

F

FAQ Schema Schema: Schema markup applied to question-and-answer content. It explicitly tells AI systems what question a piece of content answers and what the answer is. Because AI search is fundamentally question-answering, FAQ schema creates a direct machine-readable signal that your content is a candidate answer to specific queries. It is one of the highest-ROI schema types for AI visibility.

J

JSON-LD Schema: JavaScript Object Notation for Linked Data -- the format Google recommends for adding schema markup to a page. JSON-LD is written as a <script type="application/ld+json"> block in the page's HTML, keeping the structured data separate from the visible content. This makes it easier to implement and maintain than the older Microdata format. All schema on aivisible.co.uk is written in JSON-LD.

K

Knowledge Graph AI Search: Google's database of entities and the relationships between them. When Google recognises your business as an entity in its Knowledge Graph, it can connect you to related concepts, locations, people, and topics -- making it easier to surface you in relevant AI answers. Getting into the Knowledge Graph typically requires consistent structured data, third-party mentions, and a clear entity definition on your own site.
Knowledge Panel AI Search: The information box that appears on the right side of Google search results for recognised entities. It draws from the Knowledge Graph and typically shows a business's name, description, address, hours, photos, and website. Having a Knowledge Panel signals that Google has confidently identified your entity -- a meaningful trust signal for AI search systems too.

L

Large Language Model (LLM) AI Search: The AI technology that powers tools like ChatGPT, Google Gemini, and Perplexity. An LLM is trained on vast amounts of text and learns to generate coherent, contextually appropriate responses. When connected to live web search, it retrieves current content and uses it to produce cited answers. Understanding that AI search is LLM-driven helps explain why content clarity and structure matter so much.
LocalBusiness Schema Schema: Schema markup that identifies a physical or service-area business and its key details -- name, address, phone number, opening hours, geographic coordinates, and the type of business. It is the most important schema type for any business serving local customers, and the foundation on which other local schema types are built. Without it, AI systems have to infer your business details rather than read them directly.

M

Microdata Schema: An older method of adding schema markup by embedding attributes directly into HTML elements. While still valid, it is harder to read, harder to maintain, and more likely to introduce errors than JSON-LD. Google has supported JSON-LD as the preferred format since 2016. If your site still uses Microdata, migrating to JSON-LD is worthwhile.

N

NAP Local: Name, Address, Phone -- the three core pieces of identity data for a local business. Consistency of NAP across your website, Google Business Profile, social profiles, and directory listings is a foundational signal for both local SEO and entity recognition. Even small inconsistencies (Ltd vs Limited, different phone formats) create ambiguity that makes it harder for AI systems to confidently identify your business.

O

Organisation Schema Schema: Schema markup that defines a business as a recognised entity -- including its legal name, logo, contact details, social profiles, and founding information. It is the schema type that most directly supports entity recognition. Even businesses that serve local customers should have Organisation schema on their homepage, as it provides AI systems with a definitive reference point for who you are.

P

Perplexity AI Search: An AI-native search engine that generates answers with inline citations to sources. Perplexity is particularly aggressive at citing specific pages rather than just domains, making it one of the more rewarding platforms to optimise for. Its users tend to be research-minded and technically literate, making a citation there a credibility signal beyond the direct traffic it sends.
Person Schema Schema: Schema markup that defines an individual -- their name, job title, employer, credentials, and online profiles. It is particularly valuable for consultants, sole traders, and content authors, as it builds the author authority that AI systems use when deciding whether to cite a piece of content. A Person schema linked to an Organisation schema creates a credibility chain that AI systems can follow.

R

RAG (Retrieval-Augmented Generation) AI Search: The technical process most AI search systems use to answer queries. The AI retrieves relevant documents from the web or a knowledge base (retrieval), then uses those documents to generate a grounded answer (augmented generation). Understanding RAG explains why structured, clearly-written content gets cited more often -- the retrieval step selects candidates based on relevance and credibility, and the generation step needs well-organised content to extract from.
Review Schema Schema: Schema markup for customer reviews and aggregate ratings. When implemented correctly on product or service pages, it can enable star ratings in search results. However, it comes with strict rules -- you cannot mark up reviews on your own products unless they are independently submitted, and you cannot cherry-pick only positive reviews. Misuse can result in a Google manual action.
Rich Results Schema: Enhanced displays in Google search results that are enabled by schema markup -- star ratings, FAQ dropdowns, event listings, recipe cards, and so on. Rich results are the visible reward for correct schema implementation, but they are also a secondary benefit. The primary benefit is the machine-readable clarity that schema creates for AI systems, regardless of whether a rich result is triggered.

S

Schema Markup Schema: Code added to a webpage that gives machines a structured, unambiguous description of the page's content. Where ordinary HTML describes how a page looks, schema markup describes what a page means -- what type of entity it's about, what facts it contains, and how those facts relate to each other. It is the most direct way to communicate with AI search systems, because it removes the need for them to infer what your content is about.
Schema.org Schema: The shared vocabulary used to write schema markup, maintained by a collaboration between Google, Microsoft, Yahoo, and Yandex. Schema.org defines the types (Organisation, Person, Article, LocalBusiness, etc.) and properties (name, address, telephone, etc.) that schema markup can use. When you implement schema, you are speaking the language that the major search engines agreed on.
Semantic Search AI Search: Search that understands the meaning and intent behind a query, rather than just matching keywords. A semantic search engine can understand that "best plumber near me" and "local plumbing services Salford" are expressions of the same need, and that "how do I stop a dripping tap" implies an intent that a plumber could fulfil. AI search is inherently semantic, which means content needs to address topics and entities, not just include keywords.
Structured Data Schema: Any data organised in a predictable, machine-readable format. In the context of websites, structured data usually means schema markup -- specifically JSON-LD blocks that describe the content of a page. Structured data is the mechanism that translates your content from something humans read into something machines can process, index, and reason over.

T

Topical Authority AI Search: Being recognised -- by both search engines and AI systems -- as a credible, comprehensive source on a specific subject area. Topical authority is built by covering a topic in depth across multiple pages, linking those pages together logically, and earning mentions from other credible sources in the same field. An AI system with high confidence in your topical authority is more likely to cite you for any query in that space, not just queries that match a specific page.

V

Voice Search AI Search: Search conducted by speaking rather than typing, typically via a smart speaker, phone assistant, or in-car system. Voice search queries tend to be conversational and question-shaped ("Who does schema markup in Manchester?") rather than keyword-shaped ("schema markup Manchester"). The same structured data and conversational content that earns AI search citations also improves voice search visibility, because both use the same underlying technology.