The short answer
There is no such thing as an LSI keyword in the way SEO tools use the phrase. Google's John Mueller has said Google has no concept of LSI keywords and that it is something you can completely ignore. Latent semantic indexing is a real technique from information retrieval, but it was built for small, fixed document sets, not the open web.
What does work is the goal those tools were reaching for: cover a topic thoroughly, in natural language, so Google can resolve the entities on the page and see that it answers the query completely. That is semantic SEO, and it does not need a keyword list.
- Stop hunting LSI keywords. Start with the question the page should answer, then cover its real subtopics.
- Google understands entities and intent. Write for a reader who wants the whole answer, and the related terms follow.
- There is no keyword density or TF-IDF target to hit. Use those tools as coverage checks, not formulas.
Where the LSI Myth Comes From
Latent semantic indexing is genuine. It is a document-retrieval method, patented decades ago, that finds statistical relationships between terms across a small, static collection of documents. It was never a description of how a web-scale search engine ranks pages, and Google has been clear it does not use anything by that name.
"We have no concept of LSI keywords. So that's something you can completely ignore."John Mueller, Google, quoted by Search Engine Journal
The advice persisted because the outcome it promised, pages that cover a topic in depth tend to rank well, is real. The mechanism was just mislabelled. Google does not match a hidden list of related keywords. It interprets meaning. That shift has a name and a history, and it starts with the Knowledge Graph.
Things, Not Strings
When Google launched the Knowledge Graph in 2012, it described the change as understanding real-world entities and their relationships to one another: things, not strings. An entity is a distinct thing Google can recognise, a person, place, product, company or concept, and tell apart from others with the same name.
This is why context, not repetition, is what matters. Take the word Apple. On its own it is ambiguous. Surround it with iPhone, macOS and Tim Cook and Google resolves it to the technology company. Surround it with orchard, cider and harvest and it resolves to the fruit. You did not add LSI keywords. You described the subject well enough that the entity became unambiguous.
Resolves to the company
iPhone, macOS, Tim Cook, App Store, WWDC
Resolves to the fruit
orchard, Honeycrisp, cider, harvest, vitamin C
Modern ranking builds on the same idea. Google blends literal term matching with meaning-based understanding, so it can connect a query to a page that answers it even when the exact words differ. The practical instruction that falls out of this is simple: make the topic and the entities on your page clear, and cover the question fully.
How to Find the Subtopics Worth Covering
Skip the LSI keyword generators. These four sources show you what searchers and Google actually associate with a topic, so you can cover the right ground in your own words.
Related searches and autocomplete
The Related searches block at the foot of a results page, and search autocomplete, show the adjacent phrasings Google associates with a topic.
People Also Ask
The PAA box surfaces the sub-questions searchers have. Each one is a candidate subheading your page could answer.
Your own Search Console queries
The queries a page already gets impressions for are the terms Google has decided it is relevant to. Cover them properly and you reinforce the match.
Keyword research tools
Related-keyword and question reports add volume and breadth, so you can prioritise which subtopics are worth a section.
The structural companion to this is topic clusters: a pillar page on the broad subject, supported by focused pages on each subtopic, all linked together. See our guides to topic clusters and pillar pages and modern keyword research for the full workflow.
Build coverage from real queries.
Murkuz connects Google Search Console and keyword research to Claude or ChatGPT. Ask Keyword_Ideas what else people search around a topic, then ask Keyword_Rankings which queries a page already earns, so you cover the subtopics Google associates with it. It reads your data, it does not write or edit your pages.
Further reading
The sources behind this page, including Google's own statements on LSI and its Knowledge Graph announcement. Each one was opened and read in full before it was listed here.
GuideSearch Engine Journal, Miranda Miller
Latent Semantic Indexing (LSI): Is It a Google Ranking Factor?The clearest myth-buster, with Google's own words: no concept of LSI keywords, and no evidence LSI has ever been used to rank web results.
GuideSearch Engine Journal, Roger Montti
Latent Semantic Indexing Won't Help Your SEOExplains what LSI actually was in information retrieval and why it does not describe how Google interprets the web at scale.
Official docsGoogle, Amit Singhal
Introducing the Knowledge Graph: things, not stringsGoogle's own 2012 announcement of entity-based understanding. This is the idea that replaced keyword matching, and the reason semantic coverage works.
Official docsGoogle Search Central
Creating helpful, reliable, people-first contentGoogle's actual guidance. The self-assessment questions here are the real target, in place of any term-frequency formula.
GuideAhrefs, Despina Gavoyannis
Semantic SEO: What It Is and How to Do ItA modern, practical walkthrough of optimizing for meaning and topic coverage, with no mention of LSI, which is exactly right.
Frequently Asked Questions
Does Google actually use LSI keywords?
No. Google's John Mueller has said plainly that Google has no concept of LSI keywords and that it is something you can completely ignore. Latent semantic indexing is a real information-retrieval technique, but it was designed decades ago for small, fixed document collections, not the open web. The term LSI keywords, as used in SEO advice, describes something Google does not do.
If LSI is a myth, why does adding related terms still help?
Because the instinct behind the advice is right, even though the label is wrong. Google does not look for LSI keywords, but it does try to understand entities and judge whether a page covers a topic thoroughly and matches intent. Writing naturally about a subject tends to include the related terms, subtopics and entities that a genuinely comprehensive page would. You get the benefit by covering the topic well, not by inserting a list of supposed LSI terms.
What is the difference between semantic keywords and regular keywords?
A regular keyword is the literal phrase someone types, such as CRM software. Semantic keywords is a loose term for the related concepts, synonyms, subtopics and entities that naturally surround that topic, like sales pipeline, contact management or lead scoring. They are useful as a planning aid for coverage, not as strings to match. The goal is to answer the question completely, which usually means those related ideas appear on their own.
What are entities in SEO?
An entity is a distinct, real-world thing that Google can recognise and tell apart from others: a person, place, product, company or concept. Google's Knowledge Graph was built to understand entities and the relationships between them, which Google summed up as things, not strings. Establishing clearly which entities your page is about, and using the terms associated with them, helps Google categorise the page confidently.
Can a page rank for a query it never literally contains?
Yes. Google understands synonyms, paraphrases and intent, so a page can rank for queries whose exact words never appear on it. A thorough page about a topic can match many related phrasings because Google interprets meaning, not just the literal string. That is the practical payoff of covering a subject well rather than targeting one exact phrase.
How do I find related terms and subtopics to cover?
Use real sources rather than an LSI generator. Google's Related searches at the bottom of the results page and the People Also Ask box show adjacent questions and phrasings. Your own Search Console data shows the queries a page already earns impressions for, which are the terms Google associates with it. Keyword research tools add related keywords and their volumes. Then cover the subtopics those sources reveal, in natural language.
Is keyword density or TF-IDF a Google ranking factor?
There is no target keyword density, and there is no magic TF-IDF score to hit. Tools that compare your term use against top-ranking pages can be a useful coverage check, showing subtopics you may have missed. But treat the output as a prompt to cover a topic more completely, not as a formula. Over-optimizing toward a term-frequency number drifts toward keyword stuffing, which works against you.