If you’ve been told that adding “LSI keywords” will improve your search rankings, you’ve been given advice based on a 35-year-old paper about library document retrieval. Google does not use Latent Semantic Indexing. It has publicly confirmed this multiple times. The concept is valid: related words and topical context matter for search. The specific technology and most of the advice built around it are not.
What Semrush provides when it surfaces related keyword suggestions, topic clusters, and question variations is genuinely useful for SEO. It just has nothing to do with LSI. Understanding what Semrush is actually showing you, and why it matters, produces better content decisions than treating every keyword suggestion as a magic ranking signal.
Using Research Tools Correctly
Flying V Group’s keyword research tools guide covers how to use platforms like Semrush for semantic keyword research grounded in what search engines actually evaluate. The following breakdown explains the distinction that most “LSI keywords” content skips entirely.
What LSI Actually Was and Why It Does Not Apply to Google
Latent Semantic Indexing was introduced in 1990 by Deerwester et al. in the Journal of the American Society for Information Science. The method used singular value decomposition to analyze term-document matrices, finding patterns across a document collection to improve retrieval when search terms didn’t exactly match the vocabulary in the documents.
Two facts about the original research matter for SEO: it was designed for small, static document collections like library databases, and it had no mechanism for web-scale corpora where billions of documents update continuously. The term migrated into SEO vocabulary as shorthand for “related keywords” and stuck, which is how a 1990 academic paper about library science became the foundation of persistent SEO advice.
The LSI Myth Timeline
| Year | Development |
| 1990 | Deerwester et al. publish LSI paper for document retrieval systems |
| Early 2000s | SEO industry adopts “LSI keywords” as a ranking concept |
| 2013 | Google Hummingbird shifts to semantic query understanding |
| 2015 | RankBrain introduces neural learning to Google’s ranking |
| 2019 | BERT enables contextual language understanding at scale |
| 2021 | MUM advances multi-step semantic reasoning |
| Today | Topical authority and entity SEO replace “LSI” in accurate advice |
What Google Actually Uses to Understand Content
Modern Google runs on systems that bear no resemblance to 1990s information retrieval. Google’s Natural Language Processing infrastructure identifies entities, categories, sentiment, and syntactic relationships within text. The Knowledge Graph maps relationships between real-world entities (people, places, organisations, products, concepts) and uses those relationships to interpret search intent and evaluate content relevance.
BERT and MUM brought transformer-based neural models into ranking, allowing Google to understand context and how words relate to each other within a passage, not just whether specific terms are present. Google’s Helpful Content guidelines describe this directly: the ranking signal is not whether a page contains words related to the primary keyword. It is whether the content comprehensively and accurately addresses the topic in a way that demonstrates genuine expertise.
Concepts in Modern SEO vs LSI
| Concept | Does Google Use It? | What It Does |
| LSI Keywords | No | Legacy information retrieval method |
| Semantic Keywords | Yes | Related terms that signal topical coverage |
| Entities | Yes | Real-world objects and their relationships |
| Topical Authority | Yes | Depth of coverage across a subject area |
| E-E-A-T Signals | Yes | Experience, expertise, authority, trust |
| Knowledge Graph | Yes | Entity relationships and context |
What Semrush Is Actually Showing You
Semrush does not provide LSI keywords. It cannot, because Google does not use them, and there is no external signal to reverse-engineer. What Semrush provides is considerably more useful than the outdated label implies: related keywords, question variations, synonym clusters, and topic gaps derived from real search behaviour and competitor analysis.
Google’s SEO Starter Guide is clear that comprehensive, well-structured content serves users better than keyword-engineered pages. Semrush’s tools help identify where comprehensiveness is missing, not which keyword variants to insert.
Three Semrush Features That Serve Semantic SEO
Keyword Magic Tool. Search “content marketing” and the Related tab surfaces “content marketing strategy,” “content marketing examples,” and “what is content marketing.” The Questions tab pulls “how does content marketing work” and “is content marketing the same as SEO.” These are not LSI keywords. They are the actual queries people use to research the topic. A page that addresses these questions is more comprehensive, not more keyword-stuffed.
Topic Research. Enter “home insurance” and you’ll see subtopics like “types of home insurance,” “home insurance vs. renters insurance,” and “how to file a home insurance claim,” each with associated questions and trending headlines. That output is a coverage gap analysis. If competitors rank for “home insurance claims process” and you don’t have a page or section on it, that’s a topical authority gap, not a missing LSI term.
Keyword Gap Analysis. Compare your domain against two or three competitors and filter for keywords they rank for that you don’t. A cleaning company might find competitors ranking for “how often should you clean your gutters” and “seasonal cleaning checklist”, while they only rank for transactional terms. Those missing queries are intent signals, not keyword variants to insert into existing pages. They’re often worth separate content entirely.
Using Semrush for Topical Authority, Not Keyword Lists
Stop chasing LSI keywords and start looking for missing topics. Semrush’s related keywords and topic tools tell you where your content coverage ends relative to what searchers are actually researching. That gap is what SEO content strategy is actually built around.
Schema.org structured data provides a parallel signal path. Marking up entities (your business, services, geographic areas, people) gives search engines structured data that reinforces the semantic context your content communicates. This is the entity-level optimisation that has replaced keyword-pattern-matching in modern SEO.
A Semantic SEO Checklist for Each Page
- Primary keyword in title tag, H1, and opening paragraph
- Related subtopics addressed across the page or content cluster
- Entity references establishing context (who, what, where, when)
- Question-format headings covering common user queries
- Internal links to topically related pages within the cluster
- Schema markup applied where relevant (FAQ, LocalBusiness, Article, etc.)
- Search intent confirmed: informational, commercial, or transactional
Building SEO on What Actually Works
Use Semrush’s tools to find coverage gaps. Build content that addresses full topics. Apply technical SEO infrastructure and structured data to help search engines understand the entities your pages cover. Measure performance against the SEO ranking factors that actually move results: topical authority, entity clarity, and search intent match.
Contact Flying V Group to see how a semantic SEO strategy built around topical authority applies to your content programme.
Frequently Asked Questions
Do LSI keywords actually help with SEO?
No, not in the way the term is usually applied. Google does not use Latent Semantic Indexing technology. What does improve rankings is comprehensive topical coverage, entity relevance, and content that thoroughly addresses search intent. The keywords Semrush surfaces in its related keyword tools are useful for identifying coverage gaps, not for triggering LSI signals that don’t exist.
Does Semrush provide real LSI keywords?
No. Semrush provides related keywords, semantic variations, question-based keywords, and topic cluster suggestions based on actual search data and competitor analysis. The practical value is high, but it comes from what these keywords reveal about user intent and topical gaps, not from any LSI mechanism.
What did LSI keywords originally mean?
Latent Semantic Indexing was a 1990 academic method for improving document retrieval in small, static collections like library databases. It used statistical analysis to find hidden relationships between terms. It was never designed for web-scale search. The term moved into SEO as shorthand for “related keywords” and outlasted the accuracy of the concept by several decades.
What does Google use instead of LSI?
Google uses natural language processing, entity recognition via the Knowledge Graph, and transformer-based models, including BERT and MUM, to evaluate content relevance. These systems assess whether content comprehensively addresses a topic and whether the content matches what a searcher is actually trying to accomplish. Keyword variant presence is not a primary signal.
How do I use Semrush for semantic SEO without misusing LSI concepts?
Use the Keyword Magic Tool’s Questions and Related tabs to identify subtopics and user queries your content doesn’t address. Use Topic Research to find coverage gaps relative to competing pages. Use Keyword Gap to identify intent areas where competitors rank and you don’t. Treat these outputs as topic intelligence. The goal is comprehensive coverage of a subject, not keyword density.
What is topical authority and why does it matter more than LSI?
Topical authority is the depth and breadth of content coverage across a subject area. Search engines evaluate whether a site addresses a topic thoroughly across multiple angles, formats, and subtopics. Semrush’s tools are most valuable for building topical authority when used to map content clusters rather than optimise individual pages for keyword variants.
How does structured data relate to semantic SEO?
Schema.org structured data allows you to explicitly mark up entities (your business, services, locations, and content types) in a format search engines understand directly. Where keyword-based approaches rely on inference, structured data makes context explicit. Applying FAQ schema, Article schema, or LocalBusiness markup strengthens the entity signals your content sends and reduces the interpretive work search engines need to do.
