Structured Data Schema for AI-Aware SEO: Topical Pages and Hubs

Published: 08-23-2025
by Wayne Smith

This page builds on Connecting Entities with @id to show how structured data can power smarter topic hubs and category pages. Key ideas from the previous page include the role of ambiguity in language, the importance of “Gain of Knowledge” in modern SEO, and how @id links entities with the concepts they represent.

As search shifts from keyword- to concept-driven, engines increasingly match user intent with the concepts in your content. Structured links between entities make this relevance clear to LLMs and AI-guided search. Keywords still matter, but semantic clarity and unambiguous connections are essential for AI-aware SEO, since AI often uses traditional results as seed content.

Category hubs and topical pages are prime spots to implement @id properties: create clear reference points for entities, organize content hierarchically, and avoid internal competition. Done right, schema can boost visibility, enable rich results like carousels and breadcrumbs, and strengthen your site’s overall topical authority.

This page dives into how to set up @id, structure schema for category hubs, keep keyword overlap in check, and walk through real-world examples that show how semantic linking and entity hierarchy translate into clear knowledge signals and stronger visibility.

Implementing the @id Property in Schema Markup for AI-Aware SEO

In fundamental terms, the @id property connects one property of a schema to another property located elsewhere. Some have experimented with using non-standard identifiers (such as random strings) to make this connection and report that it appears to work in testing.

However, the standard does not define how a system should use the data. While a random string may function in some tests, it removes the ability for systems to reliably associate an entity with other pages of a site.

The official standard specifies using a URL or a URI. For example:

• URI: http://example.com#organization
• URL: http://example.com/organization/

According to schema.org and SEO authorities, the `@id` value is treated as an opaque identifier. It doesn’t need to resolve to a live page, and search engines don’t require it to be retrievable. If the @id is a valid, linked URL, log file analysis may show search engines attempted to crawl it.

As an unambiguous reference point, using a URI or URL makes sense. Systems can treat it as a stable identifier — either for the entire site (URI) or a page that defines the entity (URL). While schema.org doesn’t require additional content at the URL, providing it can help systems better define the entity and its site associations.

Testing the impact of using a URL versus a random string is complex. Many variables affect such tests, especially as entity usage increases. A valid test would require a statistical comparison across hundreds of pages to isolate the effect of structured identifiers. Keep in mind that systems consuming schema data may evolve over time.

Category hubs or topical pages are ideal for applying the `@id` property in modern SEO. Assigning `@id` values to category URLs creates clear reference points for topics and entities, structures content across the site, and supports well-connected topical pages.

AI-Aware Category Page Strategy: Avoiding Keyword Overlap

One of the biggest pitfalls with category pages is "content cannibalization." If your category page targets the same keywords as its sub-pages, AI-driven search that builds answers from existing results may cause you to compete with yourself.

An AI-aware SEO strategy lets the hub page focus on top-of-funnel keywords, where AI systems fan out to related subtopics, while sub-pages target specific queries and bottom-of-funnel intent. This clear division reduces overlap and protects your rankings.

Keyword cannibalization occurs when multiple pages target the same or similar keywords, making it harder for search engines to identify the most relevant result. This internal competition can confuse algorithms, lower rankings, and diminish visibility. In practice, it can make content appear unhelpful to search engines.

Google's Helpful Content Update, introduced in August 2022 and fully integrated by March 2024, prioritizes content that genuinely helps users. It evaluates quality both per page and across the site. Sites with significant unhelpful content—including those affected by keyword cannibalization—may see reduced domain-wide visibility.

To avoid this, ensure category pages and sub-pages target distinct, relevant keywords to maintain clear topical relevance and minimize internal competition.

Managing cannibalization requires ongoing monitoring as search results and algorithms evolve. Highly competitive terms are less likely to create conflicts, but careful planning and differentiated keyword targeting remain essential.

Hub Page Setup for Future Easy Wins

For category and topical pages, consider algorithmic "Contextual Authority" rather than just traditional E-E-A-T. Drawing from Google’s guidelines, rules-based AI systems can evaluate content for clarity, usefulness, and knowledge-rich value rather than relying on author credentials. Contextual Authority rewards practical, actionable insights that demonstrate expertise and real-world experience.

The "CollectionPage" schema works well for organizing topic hubs. It lists pages relevant to a topic under the hierarchy of a "WebPage." The "mainEntity" property can reference an "ItemList," which in turn includes individual "WebPages" as "hasPart" items. This structure creates clear semantic connections between the hub and its subpages.

Example Basic Skeleton JSON-LD Implementation:

The "CollectionPage" schema acts as a topic-specific site map. It helps machines distinguish which links are central to the hub and which are not, improving semantic understanding and aiding both AI-assisted and traditional search engines in interpreting topical relevance. However, the schema alone does not generate a "gain of knowledge."

Opportunities for gain of knowledge arise when using properties such as "about," "description," "headline," and "name" to clarify the entities represented on the page. "CollectionPage" is not the only type of content that can be included in a collection; other types, such as "WebPage," "Article," or "VideoObject," can also be part of the "hasPart" list, enabling richer and more semantically connected hubs.

Verbose JSON-LD Schema Example with Gain of Knowledge

The "BreadcrumbList" is useful, but schema cannot reflect site structure until linked pages are indexed. A verbose "CollectionPage" bridges this gap by signaling relationships upfront, allowing search systems to model topical context sooner and strengthen perceived authority.

Verbose schema offers additional gain of knowledge by explicitly defining entities, topics, and relationships that may not be apparent to systems interpreting only the visible content. By embedding properties such as "about," "name," "headline," and "description," the markup serves as a semantic scaffold that allows large language models (LLMs) and search systems to interpret the page with reduced risk of hallucination. This is especially valuable for category or topic hub pages, where visible content often functions as navigation rather than substantive explanation. Structured metadata enables search engines to recognize the hub’s topical scope, understand how its subpages relate, and accurately identify the entities being represented.

The "@id" property in the schema example unambiguously links all subpages to the main hub page within the "about" property.

The "name" property should use the primary entity for each subpage, often representing bottom-of-the-funnel terms related to the top-of-funnel hub topic.

The "description" property adds contextual details about each subpage entity.

Additional considerations for the description property: As noted in Modern Image SEO Using Schema Structured Data, the description does not need to be a verbatim transcript of the content. Rather, it should be semantically equivalent. While semantic HTML does not directly link a text description to a link, search engines and LLMs may use HTML proximity as a clue. LLMs also rely on linguistic signals—for example, the correct use of pronouns can help them resolve contextual relationships between entities and avoid ambiguity.