How I Moved Beyond Keyword Matching to Semantic Internal Links

How I Moved Beyond Keyword Matching to Semantic Internal Links | SEO Strategy

Introduction

Internal linking has long been a staple of SEO — but most practitioners still do it the old-fashioned way: spotting keywords, matching them to pages, and inserting links. That approach has limitations.

In this post, I’ll share how I shifted my internal linking strategy from keyword matching to semantic / embedding-based linking. I’ll explain why this matters, how I built the workflow, what challenges I faced, and the results I saw. If you run SEO audits or consult for clients (or want to scale internal linking intelligently), this method can be a game changer.

(If you want me to audit internal linking on your site, visit seohorizan.com to see how I integrate this technique in my audits.)

1. Why Keyword‑Based Internal Linking Isn’t Enough

The old way: keyword matching

Most SEOs do internal linking like this:

• Read an article or update a post
• Spot a word or phrase that matches the target keyword of another page
• Turn that phrase into a link to the “target” page

That works sometimes, but it misses many opportunities. It also overemphasizes exact keyword match, which can lead to forced anchor text, duplication, or missed connections between semantically related pages.

Google and search engines “understand meaning” now

Search engines have evolved. They rely less on literal matching of words and more on contextual understanding — represented via embeddings, semantic similarity, knowledge graphs, and concept relationships. Lumar+1

So when you link only via keyword match, you might fail to connect pages that are conceptually related but use different vocabulary. That’s a gap.

“Sentence-level semantic linking” as modern practice

Search Engine Journal wrote about a method called “sentence-level semantic internal links.” In that model, instead of looking only for keywords, you consider the meaning of the sentence or paragraph and decide whether a link would be relevant even when the keywords differ. Search Engine Journal

That’s closer to how I evolved my approach.

2. What Is Semantic Internal Linking (vs. Keyword Matching)?

Semantic linking explained

Semantic linking means connecting pages not because they share identical keywords, but because their content is topically or meaningfully related. It leverages embeddings (vector representations) of pages or text fragments, and then measures similarity (often via cosine similarity) to identify which pages are good candidates for linking.

In short: link by meaning, not just matching words. Draft&Goal+1

How embeddings / vector similarity work (at a high level)

• You convert text (a page, or a section) into a vector embedding using an embedding model/API (OpenAI, etc.).
• Each embedding is a point in high-dimensional space.
• You compute similarity (cosine similarity is common) between embeddings of candidate pages.
• Pages whose embeddings are “close enough” are semantically related even if they don’t share many keywords.

Thus you can uncover internal linking possibilities that keyword matching would never suggest.

Why semantic linking is more valuable

• Finds hidden relationships across content that use different phrasing
• Helps with topical authority by connecting conceptually adjacent pages
• Gives better UX — users can discover deeper insights through relevant links
• Future-proofs internal linking as search engines lean into embeddings and semantic models

3. My Workflow: From Content to Semantic Internal Link Suggestions

Below is the step-by-step process I developed. You can adapt it for your own site or client work.

StepDescriptionNotes / Tips1. Export & preprocess contentExtract pages/posts (or sections) into text formatRemove existing internal links to prevent bias. Optionally chunk large pages (by heading or section).2. Generate embeddingsUse an embedding model/API (OpenAI, other) to vectorize each page or chunkStore embedding alongside metadata (URL, title, section)3. Build or use a similarity indexUse a vector database or library (e.g. Pinecone, FAISS)Enables fast nearest‑neighbor queries4. For each “anchor page,” query similar pagesChoose a “source” page (or section) and find top‑K semantically similar pagesFilter out self, pages already linking, or below similarity threshold5. Vet & select link candidatesManually review suggestionsPick candidates that make sense in context; avoid spurious links6. Choose anchor text & placementInsert internal links into relevant sentences / paragraphsMake anchor natural and descriptive7. Monitor, iterate & refreshTrack results (traffic, engagement), and refresh embeddings when content updatesEvolve thresholds and tactics over time

My personal observations / parameters

• I used a similarity threshold of ~0.65–0.80 (depending on embedding model) as a lower bound.
• I chunked large pages (e.g. long tutorials) into sub‑sections so that embeddings stayed focused and linking suggestions stayed meaningful.
• In several cases, pages that had zero textual overlap (i.e. no shared keywords) were surfaced as good internal link candidates — surprising but helpful.
• I filtered out “already linked” pages and low traffic / low content pages to reduce noise.

4. Challenges & How I Handled Them

False positives / irrelevant suggestions

Not every embedding neighbor is link-worthy. Sometimes pages are semantically similar but not appropriate to connect. → I manually vet suggestions, and I set a stricter threshold if too many poor matches appear.

Content that covers multiple topics (topic drift)

Some pages are broad or multi‑theme, making embedding less precise. → I chunk content or isolate subsections so embeddings reflect narrower topics.

Anchor text over-optimization / repetition

Since semantic linking sometimes suggests many links, there’s risk of anchor text becoming repetitive or unnatural. → I diversify anchor text and avoid stuffing.

Syncing with content updates

If pages are updated or new pages added, embeddings get stale. → I schedule periodic re-embedding and re-evaluation (quarterly or on content updates).

Scalability / performance

For large sites, computing embeddings and querying at scale is resource-intensive. → Use efficient vector indices, incremental updates, and caching.

5. Results & Anecdotes

Here’s what I observed when I rolled out this method on client / personal projects:

• Pages that previously had no internal links were suggested as link candidates, and after linking, those pages saw a lift in organic traffic.
• Some link suggestions were counterintuitive — pages that seemed unrelated by title but shared conceptual overlap.
• Users spent more time exploring semantically connected content, leading to lower bounce and deeper site paths.
• Over time, internal link structure became more coherent — concept clusters became more tightly interlinked.

These outcomes confirmed that moving beyond keyword matching unlocked more valuable internal link opportunities.

6. How You Can Apply This (For Your Site or Clients)

Here’s how you can experiment with this method:

1. Start small — pick a section of your site (e.g. blog, tutorial section) and extract text.
2. Use a free or low-cost embedding API to vectorize pages or sections.
3. Use an open-source vector index (FAISS, Annoy) or a managed vector db (Pinecone) to index embeddings.
4. Run similarity queries and generate link suggestions.
5. Manually vet and insert a few internal links.
6. Monitor results (traffic, click paths) over a few weeks.
7. If positive, expand to more content portions and automate periodic refreshes.

You can integrate this into your SEO audit service — show clients internal linking opportunities that keyword-based methods would miss.

Conclusion

Switching from keyword matching to semantic internal linking might feel like a leap — but it allows you to uncover deeper, more meaningful connections across your content. It aligns better with how search engines understand meaning, and gives your users more intuitive navigation.

If you’d like me to help you audit internal linking on your site (with semantic techniques), or set up embedding-based systems, I’d be happy to assist. Just say the word, and I can adapt this blog further or craft a service pitch for seohorizan.com.