For most of SEO’s history, internal linking strategy was simple: find a keyword in your body text, link it to a related page, and move on. This worked when Google’s algorithm was fundamentally a keyword-matching engine. It no longer is. Google’s understanding of content has evolved from matching strings to understanding entities — and your content strategy and internal linking need to evolve with it.
I have implemented entity-based internal linking strategies across dozens of clients throughout my career, and the pattern is consistent: sites that structure their internal links around entity relationships outperform sites that rely on keyword-match linking. The difference is measurable, repeatable, and accelerating as Google’s NLP capabilities improve.
What entity-based linking actually means
An entity, in Google’s knowledge graph, is a thing — a person, a place, a concept, a product, a condition. Entities have attributes and relationships to other entities. When Google crawls your site, it is not just matching keywords to pages. It is building a graph of what entities your site covers, how those entities relate to each other, and how authoritative your coverage of each entity is.
Entity-based internal linking means structuring your links to reflect these relationships explicitly. Instead of linking the phrase “technical SEO” to your technical SEO service page every time it appears in body text (keyword-match linking), you link it when the surrounding context establishes a meaningful entity relationship — when the content is discussing a concept that your technical SEO page covers authoritatively.
The difference seems subtle, but the impact on topical authority is substantial. Keyword-match linking tells Google “these two pages share a keyword.” Entity-based linking tells Google “this page is part of a coherent body of expertise on this topic.” The second signal is what builds the topical authority that Google rewards with higher rankings across your entire content cluster.
The reverse content silo: entity linking in practice
The implementation framework I use is called a reverse content silo. The concept is straightforward: you designate a Target Page (typically a service or conversion page targeting a competitive keyword), then create supporting content that links to that Target Page and to each other in a structured pattern.
Each supporting piece links to the Target Page near the top of the content, and to one or two other supporting pieces. The supporting pieces do not link to any other pages outside the silo. This creates a concentrated flow of relevance signals from the supporting content to the Target Page, telling Google that this cluster of content represents deep, focused expertise on the Target Page’s topic.
The “reverse” designation comes from the flow direction — link equity flows from the supporting content downward to the Target Page, rather than from a hub page outward to subpages. This is the opposite of traditional site architecture thinking, but it aligns better with how Google evaluates topical authority.
Why this matters for AI visibility
Entity-based content architecture has an additional benefit that has become increasingly important: it improves AI citation visibility. When AI systems like Google’s AI Overviews, ChatGPT, or Perplexity crawl your site, they are attempting to map your content to their internal knowledge graphs. If your content is structured around clear entities with explicit relationships, the mapping is more accurate and more likely to result in your content being selected as a citation source.
This is why I now consider entity-based internal linking and structured data implementation as two sides of the same coin. Your internal links define entity relationships at the content level. Your JSON-LD schema defines entity relationships at the metadata level. When both are aligned, you are giving Google’s systems redundant, reinforcing signals about what your content covers and how it relates to the broader knowledge graph.
Implementation: a real example
For a recent client in the B2B healthcare space, I built a reverse content silo targeting their primary service page. The silo consisted of 13 supporting blog articles, each answering a specific question that their target audience searches for. Each article linked to the Target Page using descriptive anchor text that reflected the entity relationship, and to one or two other articles in the silo.
Within 4 months, the Target Page moved from position 14 to position 3 for the primary keyword, and the supporting articles collectively drove 40% more traffic than they had as standalone, unlinked content. The silo structure concentrated the topical authority that had been dispersed across the site into a focused signal that Google rewarded.
Common mistakes
The most common mistake I see is over-linking. If every page links to every other page, you have not created topical clusters — you have created noise. The silo works because it is selective. Each supporting piece links to the Target Page and one or two related supporting pieces. That is it. No links to unrelated pages, no footer link blocks, no sidebar widgets full of internal links. The constraint is the strategy.
The second mistake is using exact-match anchor text on every link. This is a relic of keyword-match thinking. In an entity-based framework, your anchor text should vary naturally while still describing the entity relationship. “Our content strategy methodology,” “the entity-based approach we use for content architecture,” and “building topical authority through strategic content” are all valid anchor text variations that describe the same entity relationship to the target page.
Keyword matching had its era. Entity-based internal linking is what works now, and the gap between the two approaches will only widen as Google’s understanding of content continues to improve.