Free LSI & Semantic Keyword Finder
A free semantic keyword finder that pulls related terms and entities from Google, Wikipedia and competitor pages, then checks what your draft misses.
What is a semantic keyword finder?
A semantic keyword finder lists the terms, phrases and named entities that naturally appear around a topic, so your content covers it the way searchers and search engines expect. This tool combines live Google Autocomplete phrases, the matching Wikipedia article and its linked entities, and any competitor text you paste, then scores each term.
Write about “intermittent fasting” without mentioning eating windows, insulin, the 16:8 method or calorie intake and the page feels thin to a reader. It also looks thin to Google, which has spent more than a decade learning how topics, entities and their attributes connect. The terms this tool surfaces are the vocabulary of a topic. They are not magic ranking words, and you should not paste them into a page as a list.
Are “LSI keywords” real?
Not in the way most SEO blogs describe them. Latent semantic indexing is a 1980s information-retrieval method, and Google has said it does not use “LSI keywords”. What it does use is semantic and entity understanding, through systems like the Knowledge Graph and BERT, to work out what a page is actually about.
We kept “LSI” in the name because that is what people search for. The honest version is this: Google doesn’t count related words, but pages that explain a topic properly end up using the related words anyway. If your guide to running shoes never mentions cushioning, heel drop, pronation or stack height, it probably isn’t a good guide. Use the output as a coverage checklist written by the topic itself.
A quick example from our own testing: for “intermittent fasting”, the Wikipedia article and autocomplete together surface terms like time-restricted eating, alternate-day fasting, insulin, calorie restriction and weight loss. A page that explains each of those is more useful than one that repeats the main keyword twenty times, and that is the whole point.
| Concept | What it really is | What to do with it |
|---|---|---|
| LSI (latent semantic indexing) | A 1988 Bellcore technique for matching documents by co-occurring words | Ignore the label, keep the idea of topical vocabulary |
| Semantic keywords | Phrases searchers use around a topic, often found in autocomplete | Use as subheadings, FAQ questions and anchor text |
| Entities | Distinct things such as people, places, products and concepts | Name them clearly and explain how they relate |
| TF-IDF terms | Words that are frequent on top pages but rare in general text | Spot gaps in your draft, not a density target |
Features
Eight live lookups (“what is”, “vs”, “for”, “types” and more) capture how people phrase the topic right now.
The tool finds the matching article, reads its text and the topics it links to, and ranks those entities by how often they appear.
Stopwords are removed and overlapping phrases merged, so you get “heel drop” and “stack height” instead of “the” and “of the”.
Paste the text of up to five ranking pages. Terms used by several of them score higher, which is the useful part of TF-IDF.
Paste your draft and every term is marked as covered or missing, with a coverage score for the top 30 terms.
Copy terms or entities, or download a CSV with scores, sources and mention counts for your content brief.
How to find semantic keywords
1. Enter your topic
Use the main keyword of the page, such as “intermittent fasting”. A topic with its own Wikipedia article gives the richest entity list.
2. Add competitor text
Optionally paste the main copy of the top three to five Google results, separated by lines with three dashes.
3. Paste your draft
Optionally paste your own draft so the tool can flag high-scoring terms you have not used yet.
4. Review and export
Sort by score, check the Entities tab, then copy the terms that genuinely belong in your outline or download the CSV.
How should you use semantic keywords in content?
The best results we see come from treating the list as an editor’s checklist, not a stuffing quota. A workflow that holds up in practice:
- Sort into roles. Entities become sections or definitions, autocomplete phrases become H2s or FAQ questions, and frequent n-grams become supporting detail.
- Write first, check second. Draft from expertise, then paste the draft in and look only at missing terms that score in the top 30.
- Explain relationships. “Insulin levels drop during a fast” is worth more than mentioning insulin three times. Relationships are what entity-based search and AI Overviews pick up.
- Watch topical authority. If an entity deserves 800 words on its own, give it a separate page and link to it. That is how topic clusters form.
One limitation worth knowing: stopword removal and n-gram scoring work best in English. Other languages still return autocomplete phrases and Wikipedia entities, but expect a few more filler words in the n-gram list.
Frequently asked questions
Is this LSI keyword tool free?
Yes. It is completely free with no sign-up or API key. Each run makes about ten lookups, well inside the fair-use limit of 60 per minute.
Does Google use LSI keywords?
No. Google has said it does not use latent semantic indexing. It uses semantic and entity understanding, so covering related concepts helps because it makes the page more complete, not because of a keyword formula.
Where do the semantic keywords come from?
From live Google Autocomplete suggestions, the matching Wikipedia article and the entities it links to, and any competitor text you paste. Nothing is generated from a fixed word list.
What is the score column?
A relative 0 to 100 score that weights how often a term appears, how many sources use it, phrase length and whether it is a Wikipedia entity. It is an estimate for prioritising, not a Google metric.
How many semantic keywords should a page use?
There is no target number. Cover the terms a knowledgeable writer would naturally use. For most articles, that means most of the top 20 to 30 terms, used where they make sense.
Is my pasted text stored?
No. Competitor text and your draft are analysed in your browser and never sent to our server. Only the seed keyword is used for the Autocomplete and Wikipedia lookups.
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Last updated: · By AM Digital Marketing Agency SEO Team
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