{"id":4476,"date":"2025-12-11T18:33:17","date_gmt":"2025-12-11T18:33:17","guid":{"rendered":"https:\/\/keywordseverywhere.com\/blog\/?p=4476"},"modified":"2026-04-13T18:22:11","modified_gmt":"2026-04-13T18:22:11","slug":"lsi-latent-semantic-indexing-keywords-in-seo","status":"publish","type":"post","link":"https:\/\/keywordseverywhere.com\/blog\/lsi-latent-semantic-indexing-keywords-in-seo\/","title":{"rendered":"LSI Keywords (No, Google Doesn\u2019t Use Them) \u2014 Here\u2019s What to Do Instead"},"content":{"rendered":"\n

Let\u2019s clear something up straight off the bat: Google doesn\u2019t use LSI keywords<\/strong>, and it never has. The reason the term never really died down is because its underlying practice technically works. Let\u2019s explore what LSI keywords actually are and how the idea behind them can still prove useful for your SEO strategy<\/a>.<\/p>\n\n\n\n

<\/a>What are LSI keywords in SEO?<\/h2>\n\n\n\n

The technology of Latent Semantic Indexing (LSI) is based on a real information retrieval method patented in 1989 by researchers at Bell Labs, including Susan Dumais.<\/p>\n\n\n\n

LSI keywords have come to be understood as words or phrases related to the target keyword. For example, if your primary keyword is \u201ckeyword research\u201d, related terms might include \u201cSEO\u201d, \u201cseed keywords\u201d, \u201ccontent creation\u201d, or \u201csearch intent\u201d.<\/p>\n\n\n\n

Human beings find it effortless to associate words based on context. If someone mentions \u201ccoffee shop\u201d, we can instinctively think up all the words that fit the context\u2013 \u201cbrew\u201d, “latte\u201d, \u201cchill music\u201d, even \u201cafternoon chitchats\u201d.<\/p>\n\n\n\n

Computers, on the other hand, need \u201ctraining\u201d to understand word associations and meaning. This training is what Latent Semantic Indexing is all about. This concept does sound helpful, like a promising method that could help search engines move beyond keyword density.<\/p>\n\n\n\n

<\/a>Why do people think Google uses LSI keywords?<\/h2>\n\n\n\n

In its infancy, Google heavily relied on exact-match keywords. Thus, marketers had to obsess over a single keyword to the point of keyword stuffing in an effort to get good rankings. Fast forward today, Google has gone so much smarter. However, it was never indicated that they\u2019re using LSI Keywords or adopting it as a ranking factor.<\/p>\n\n\n\n

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There’s no such thing as LSI keywords\u2013 anyone who’s telling you otherwise is mistaken, sorry.<\/strong><\/p>\n\u2014 John Mueller<\/a><\/cite><\/blockquote>\n\n\n\n

The closest thing Google launched which resembles LSI keywords is the Hummingbird update<\/strong>.<\/p>\n\n\n\n

Released in 2013, it was considered a major overhaul of Google\u2019s search algorithm, focusing on overall meaning and context instead of a rigid match of keywords. The Hummingbird update improved how Google understood intent, processed natural language like how humans do, and made sense of related concepts even without keyword matches.<\/p>\n\n\n\n

When marketers realized that simply stuffing one keyword no longer worked, they sought out new terminology to stress the importance of using synonyms and related terms. The SEO community stumbled upon the concept of \u201cLSI keywords\u201d, which seemed to be a catchy and technical-sounding shorthand for the practice.<\/p>\n\n\n\n

SEO influencers also hyped it up, attributing their success to the concept, and actual tools were even built around the term itself.<\/p>\n\n\n\n

It\u2019s fairly understandable why the term quickly became a hot topic and a buzzword. But then again, Google repeatedly denied the use of this technology.<\/p>\n\n\n\n

<\/a>If not LSI Keywords, then what?<\/h2>\n\n\n\n

Google does not use Latent Semantic Indexing because it\u2019s an old technology, invented in the 1980s before the creation of the World Wide Web. It was only designed for smaller documents and static datasets and couldn\u2019t possibly cover the entire web.<\/p>\n\n\n\n

Google developed better and more scalable technology to understand queries and content contextually at scale. In particular, Google uses other sophisticated methods like:<\/p>\n\n\n\n

<\/a>1.    Vector Embeddings<\/h3>\n\n\n\n

The outdated LSI technology is based on a sheer statistical approach. Relying too much on keyword co-occurrence and keyword density, it didn\u2019t have the ability to truly grasp context and meaning.<\/p>\n\n\n\n

Vector-based embeddings enable meaning-based retrieval instead of keyword-based retrieval. The approach is based on a mathematical technique that translates words and concepts into vectors or numerical representations that machines can understand. Words with similar meanings are then mapped in closer proximity in a multi-dimensional or vector space. Terms with different meanings are positioned farther apart. Proximity thus implies semantic similarity. When a user fires a query, Google then instantly calculates the distance between the query vector and all the stored webpage vectors in the database.<\/p>\n\n\n\n

<\/a>2.    Knowledge Graph<\/h3>\n\n\n\n

Google introduced Knowledge Graph as early as May 2012. Its main function is to organize all unstructured data into a structured format, sorting \u201centities\u201d (people, places, concepts, facts) and mapping the relationships between them.<\/p>\n\n\n\n

For instance, it doesn\u2019t just process \u201cBarack Obama\u201d and \u201cHawaii\u201d as separate keywords, but it would know that Barack Obama was born in Hawaii. This relational database model brings order to disparate facts.<\/p>\n\n\n\n

This organization and retrieval tool also avoids disambiguation. For instance, it can understand that \u201cMercury\u201d might be a planet, a metal, or a deity\u2014and choose the right one based on context.<\/p>\n\n\n\n

Because of how fast, direct, and contextually relevant the resulting facts and answers are, the Knowledge Graph is powering lots of applications, including the Knowledge Panels, Voice Assistants, SERP Features, and the semantic Google Search itself.<\/p>\n\n\n\n

<\/a>3.    Natural Language Processing (NLP) and Machine Learning Models<\/h3>\n\n\n\n

Vector embeddings are powered by machine learning models to learn the nuance of human context and language.<\/p>\n\n\n\n

Google leverages the full power of modern NLP and AI to mimic human comprehension, helping it understand what users mean, not just the words they use. It builds upon the following AI systems, along with other features and signals:<\/p>\n\n\n\n

<\/a>\u25cf      RankBrain<\/h4>\n\n\n\n

Google\u2019s main purpose is to organize the world\u2019s information and serve the best and most relevant results. However, it had always been a challenge how 15% of daily queries\u2013 that\u2019s billions of queries per day\u2013 were novel and never-been-seen-before.<\/p>\n\n\n\n

RankBrain solved this gigantic issue, deciphering anything novel, vague, and unclear. Even if users aren\u2019t using the right vocabulary, RankBrain can predict what the user meant. Because of how it effectively nails search intent, it\u2019s an important component of Google\u2019s ranking system. It helps decide the best order for top search results.<\/p>\n\n\n\n

Besides search intent, this machine learning model also looks at UX signals to find patterns that indicate relevance, including click-through rate (CTR) and dwell time. It helps interpret the whole search landscape to rank results in the most optimal order for users.<\/p>\n\n\n\n

<\/a>\u25cf      BERT<\/h4>\n\n\n\n

In 2019, Google further enhanced semantic search with the introduction of BERT. Traditional NLP models could only process words sequentially (either left-to-right or right-to-left), which often ends up missing the full context of the sentence. BERT understands nuance bidirectionally, interpreting words that come before and after. Even prepositions and subtle connectors are accurately interpreted. BERT is not a replacement for RankBrain but an additional method in Google\u2019s toolbox to further deliver the most helpful and relevant results.<\/p>\n\n\n\n

<\/a>\u25cf      MUM<\/h4>\n\n\n\n

There are certain queries that require significant context and research across multiple languages, steps, and formats.<\/p>\n\n\n\n

Take this query as an example:<\/strong><\/p>\n\n\n\n

\u201cI climbed Kilimanjaro last October and now want to do Everest Base Camp next spring. What should I do differently to prepare?<\/p>\n\n\n\n

It\u2019s so complex and multi-layered that it demands a hyper-efficient system for comparing weather conditions, altitude, trail difficulty, gear and equipment, etc. MUM or Multitask Unified Model is created exactly for this function. It\u2019s designed to synthesize information from various sources, trained across 75 languages, and meant to be multimodal, understanding various formats such as text, images, audio, and video. MUM is a thousand times more powerful than BERT, but it\u2019s not meant to replace either RankBrain or BERT. Instead, it\u2019s an added layer of intelligence to enhance the search process.<\/p>\n\n\n\n

Though MUM is highly advanced, it\u2019s also super resource-intensive, so Google doesn\u2019t really use it for every single query. It only reserves it for highly complex searches that require deep, multi-step reasoning and cross-domain understanding.<\/p>\n\n\n\n

<\/a>\u25cf      Neural Matching<\/h4>\n\n\n\n

Neural matching is another NLP system that allows Google to process and understand language at a deeper, more human-like level. It’s the system responsible for calculating the proximity of vectors using mathematical methods like cosine similarity. This helps Google serve the most relevant search results even if the query is expressed with alternate terms of vocabulary.<\/p>\n\n\n\n

It\u2019s mainly a sophisticated retrieval engine that reduces the time it takes for users to find the information they need, making the overall search process more intuitive and efficient.<\/p>\n\n\n\n

The NLP and AI systems above complement each other within Google’s overall algorithm. They\u2019re also largely built on the same transformer architecture that Google introduced in 2017. Google did not invent LSI technology, but it definitely became the driving force behind the biggest breakthroughs in semantic search. In fact, its Transformer neural network architecture became the foundation for almost all modern large language models (LLMs), including Google’s own BERT and GPT-3 and beyond.<\/p>\n\n\n\n

Google has definitely come a long way in creating a more sophisticated and useful search engine that we now all enjoy.<\/p>\n\n\n\n

<\/a>Can LSI keywords still be helpful?<\/h2>\n\n\n\n

The quick answer is yes. LSI keywords\u2019 benefits are real. While the term “LSI keywords” is incorrect, the underlying concept of using related vocabulary and context is central to how modern Google Search works. It\u2019s then fair to consider LSI as an ancestor to modern search engine technology.<\/p>\n\n\n\n

The more accurate term for LSI keywords is \u201csemantic keywords\u201d, supporting words and phrases that improve topical depth, semantic relevance, and intent alignment.<\/p>\n\n\n\n

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“Google does like synonyms and semantics, but they don\u2019t call it Latent Semantic Indexing. For an SEO to use those terms can be misleading and confusing to clients who look up Latent Semantic Indexing and see something very different. There is no Wikipedia information on LSI Keywords. There is no information about how LSI Keywords might use LSI. There are no patents that explain how LSI Keywords work because they have never been patented.”<\/strong><\/p>\n\u2014 Bill Slawski<\/cite><\/blockquote>\n\n\n\n

It\u2019s clear that Google always favors pages that satisfy search intent and relevance criteria. And although technically not \u201cLSI keywords,\u201d using relevant keyword phrases and variations helps search engines draw logical relationships and better understand the meaning and context of a page.<\/p>\n\n\n\n

Besides other signals like backlinks, the presence of semantically related terms can help Google understand content at such a deep level. For instance, if you write an article about \u201clink building\u201d, other terms that commonly appear when you talk about the topic extensively include \u201cguest posting\u201d, \u201ccitations\u201d, \u201cdirectories\u201d, \u201coutreach\u201d, or even \u201cHARO\u201d.<\/p>\n\n\n\n

The deeper you explore a topic, the more terms and jargons surface, and that signals Google how fully developed your content is. Thus, if you only talk about a topic at a surface level, Google would also know because of the lack of any related and important terms that indicate breadth and depth. You can also dive into Google\u2019s research paper<\/a> and their \u2018How Search Works\u2019 overview<\/a> to better understand how these signals work.<\/p>\n\n\n\n

<\/a>How to Identify and Use (LSI) Semantic Keywords<\/h2>\n\n\n\n

Whether you call them LSI keywords or not, adding related words and phrases can help improve your content\u2019s relevance, boost your discoverability, and increase your search engine rankings<\/a>.<\/p>\n\n\n\n

And since you\u2019re already producing content anyway, might as well do it right and take the time to optimize it the best way you can. Let\u2019s talk about how you can find and use these semantic keywords that can make your content extra discoverable and effective.<\/p>\n\n\n\n

<\/a>How to find (LSI) semantic keywords<\/h3>\n\n\n\n

Before we talk about where you can find all these semantic keywords, let\u2019s further cite some examples and distinguish them from the misattributed LSI keywords.<\/p>\n\n\n\n

<\/a>LSI vs Semantic Keywords<\/h4>\n\n\n\n

Since people always confuse LSI keywords with semantic keywords, you may assume they\u2019re just one and the same.<\/p>\n\n\n\n

Here are semantic and lsi keywords examples and how they differ.<\/p>\n\n\n\n

Aspect<\/strong><\/td>LSI Keywords<\/strong><\/td>Semantic Keywords<\/strong><\/td><\/tr>
Scope<\/td>Specific word relationships<\/td>Broader conceptual relationships<\/td><\/tr>
Focus<\/td>Based on co-occurrence patterns<\/td>Focused on overall meaning and intent<\/td><\/tr>
Example<\/td>“Pizza” \u2192 “crust,” “sauce,” “cheese”<\/td>“Healthy eating” \u2192 “balanced meals,” “nutrition,” “wellness”<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

If you take a closer look, LSI keywords are just citing basic, surface-level relationships like something fresh out of a thesaurus, e.g. cook\u2192 fry, boil, roast, bake. They\u2019re all about words and how frequently they appear together.<\/p>\n\n\n\n

Semantic keywords, on the other hand, touch on concepts, variations, related subtopics, and even history. Full context is covered, leaving little to no room for ambiguity. For example, if the topic is about cooking, other words and phrases that are conceptually related can be interpreted such as famous chefs (Julia Child, Wolfgang Puck), history of culinary arts (Escoffier\u2019s brigade system, the rise of Nouvelle Cuisine), recent events (Netflix food-culture documentaries, global Michelin star announcements), or even cultural influences (Italian regional cooking, Japanese kaiseki traditions).<\/p>\n\n\n\n

Semantic keywords are more than co-occurrence patterns. They\u2019re full-spectrum understanding and framework.<\/p>\n\n\n\n

<\/a>How to Find Semantic Keywords<\/h4>\n\n\n\n

Now, how do you find these semantic keywords to start optimizing your content the best way possible?<\/p>\n\n\n\n

Even without a keyword research tool, you can find all these terms free of charge. Just check the following places on Google:<\/p>\n\n\n\n

<\/a>\u25cf       Google Autocomplete<\/h5>\n\n\n\n

These autocomplete suggestions are based on actual, real-world searches, which would make your content extra helpful for more people and more relevant across variations of the query.<\/p>\n\n\n\n

Whether you\u2019re looking for more insights to develop your outline or finalize your keyword or even topic cluster, this can be your quick and easy brainstorming tool.<\/p>\n\n\n\n

You can even validate your keywords on the go by installing our Keywords Everywhere browser extension to instantly view the search volume and other keyword-level data of the autosuggested terms.<\/p>\n\n\n\n

\"Autocomplete\n\t\t\t\n\t\t\t\t\n\t\t\t<\/svg>\n\t\t<\/button>
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