Latent semantic indexing: optimize content for today’s SEO

Sponsored by Semrush, written by Veruska Anconitano & edited by Nichola Stott

Over the years, latent semantic indexing (LSI) and latent semantic indexing keywords (LSI keywords) have been among the most debated issues among SEOs and marketers. There’s still no clear consensus on whether LSI and LSI keywords in the context of search engine marketing are real.

If you’re new to latent semantic indexing, this guide will give you a clear overview. It explains LSI’s history and how it affects—or doesn’t affect—modern SEO.

What is latent semantic indexing?

Latent semantic indexing is a mathematical method for analyzing large datasets to understand the closeness of words and concepts. It helps information retrieval systems understand connections between words and how they fit together into concepts.

In other words, let’s say you search for “healthy eating.” A document might not use the exact phrase “healthy eating,” but instead uses terms like “balanced diet,” “nutritious meals,” or “whole foods.” These terms aren’t exact synonyms, but LSI sees them as related. This is because they often appear together in documents about nutrition, even if they weren’t in the original search.

LSI analyzes co-occurrence patterns of words across multiple documents (in simple terms, how often words appear together in different documents), identifying latent structures beyond simple keyword matching. For example, it groups related terms like “automobile” and “car” based on their frequent use in similar contexts. This helps systems better understand the overall meaning of the content, especially in large-scale information retrieval.

Developed in the late 1980s, latent semantic indexing marked a breakthrough in addressing two significant challenges in text search and information retrieval: polysemy (where a word has multiple meanings) and synonymy (where different words share the same meaning).

Polysemy

bank your money, or river bank

Synonym

bank and vault

These issues often lead traditional keyword-based systems to deliver poor or irrelevant results because they rely too heavily on exact keyword matches. LSI solves this problem by identifying patterns of word usage and co-occurrence. It allows a system to infer the semantic context of a query and to detect hidden relationships between terms, even if those terms didn’t co-occur frequently in the same documents or when the precise keywords used are absent from the relevant documents.

The mathematical foundation of latent semantic indexing

At the heart of latent semantic indexing is a mathematical process called singular value decomposition (SVD). SVD breaks a large matrix into three smaller ones to reveal hidden relationships between terms and documents. In LSI, this matrix is the term-document matrix. Each row is a term and each column is a document, with the values showing how often terms appear in those documents.

How singular value decomposition works

Singular value decomposition breaks down this large matrix into three smaller matrices:

1. U (terms): a matrix representing terms and their relationships to latent concepts
2. Σ (singular values): a diagonal matrix that highlights the strength of each latent concept
3. V (documents): a matrix representing documents and their relationships to the latent concepts

Through this process, LSI converts raw term-document data into a more abstract space. This allows it to detect relationships between terms that aren’t directly connected in the original data.

Here’s an easy example of how this might look. Imagine we have three documents:

• Document 1: “The cat plays with yarn”
• Document 2: “The dog chases the cat”
• Document 3: “The cat sleeps on the couch”

First, we create a term-document matrix with the terms (words) as rows and the documents as columns. This matrix captures how many times a term appears in a document.

Using singular value decomposition (SVD), the matrix is split into three smaller matrices. This helps uncover hidden relationships between words and documents.

If someone searches for “yarn,” LSI can recognize that in Document 1, “yarn” and “cat” appear together. So, even if another document only mentions “cat,” LSI understands there’s a connection between the two terms.

It’s like finding hidden topics that link similar words and ideas across multiple documents, making search results more contextual and relevant.

This process enables better understanding of related terms (like “dog” and “cat”) and how documents are tied to broader topics.

The strength of latent semantic indexing is in detecting hidden semantic relationships in data. It goes beyond simple keyword matching to uncover patterns of meaning. 

LSI groups semantically similar words by analyzing how terms co-occur across many documents, even if they aren’t direct synonyms. This helps systems better understand the context of terms and retrieve documents that are conceptually related to a query, even without exact keyword matches.

For example, a search engine using LSI can deduce that someone searching for “digital camera” might also be interested in documents about “photography equipment” or “camera reviews.” This happens because LSI looks at how terms appear together in different documents, rather than treating each word separately.

While latent semantic indexing is mathematically sound and effective in detecting patterns based on how often words appear together, it can’t truly understand the meaning behind those words. This approach relies purely on statistical co-occurrence without considering the deeper context or intent. It’s a structured, rule-based method that classifies terms based on their presence in documents rather than their semantic significance or the actual relationships between concepts.

As a result, while LSI can group related terms, it doesn’t fully grasp what those terms truly represent in different contexts.

How LSI relates to SEO and modern search engines

In the early days of search engine optimization, latent semantic indexing was considered a revolutionary approach to improve the way search engines understood the context and relationships between words.

Search engines were initially focused on keyword matching. Still, LSI promised to address latent semantic structures—patterns hidden within large datasets of text—to deliver more relevant search results, even when the exact terms used in the search query weren’t present in the indexed documents.

For example, LSI could infer that the words “automobile” and “car” are semantically related, even if some documents used only one term and not the other. By bridging these gaps, LSI improved the retrieval of relevant documents that keyword-only search systems would have missed.

The myth of LSI keywords in SEO

During the early 2000s, SEO professionals began to speculate that LSI keywords—terms conceptually related to primary keywords—could enhance website rankings. The belief was that search engines were using LSI or similar techniques to understand the meaning behind web content. As a result, many SEO strategies focused on inserting related terms, assuming this would improve the relevance of a page and make it more attractive to search engine algorithms.

This approach, however, was based on a misunderstanding of how search engines operated. While the idea of semantic relationships was valuable, the belief that search engines were specifically using LSI was incorrect.

Despite its initial promise and the persistence of the term “LSI keywords” in some SEO discussions, LSI and LSI keywords are not part of modern search engine algorithms and do not exist as a distinct ranking factor.

In 2019, John Mueller, a representative of Google, confirmed that LSI keywords do not exist in Google’s algorithm. He clarified that while the idea of using related terms is valuable, it’s not because Google uses LSI​.

As search technologies evolved, especially with the development of machine learning and natural language processing (NLP), search engines like Google moved away from LSI in favor of more advanced methods to analyze user intent and the meaning behind queries.

With that being said, Google cares about semantics. A lot. Search engines today rely on far more advanced approaches to determine the semantic relevance of content. 

Instead of using LSI, search engines use natural language processing (NLP) and machine learning models like BERT (Bidirectional Encoder Representations from Transformers). These models help search engines understand the context, relationships between concepts, and user intent behind search queries.

What is natural language processing?

While latent semantic indexing focuses on finding relationships between terms, natural language processing helps machines understand, interpret, and generate human language. NLP is essential in modern search engines, allowing them to grasp the words users type and the intent and context behind them. By parsing language nuances, resolving ambiguities, and interpreting user intent more precisely, NLP enhances the overall search experience.

Natural language processing goes much further than LSI. NLP doesn’t just analyze how often words appear together but also understands syntax, semantics, and context. With NLP, search engines can:

• Interpret user intent: NLP helps search engines understand what a user is really asking for, even if the words they use are ambiguous or uncommon
• Resolve language ambiguities: NLP interprets words like “bank” (financial institution) and “bank” (riverbank) differently based on their surrounding words and context
• Grasp the full meaning of sentences: NLP can discern the complete context of sentences even if the query doesn’t use exact keywords from the indexed content

For example, if a user searches for “how to change a car tire,” NLP allows the search engine to understand the action (changing) and the object (car tire), delivering results that explain how to do the task, not just pages with the keywords “change” and “car tire.”

The differences between NLP and LSI

There are some key differences between natural language processing and latent semantic indexing:

• LSI focuses on word relationships based on frequency and co-occurrence, while NLP understands the intent and meaning behind those words
• LSI is limited to analyzing terms in relation to each other, while NLP uses machine learning models like BERT to grasp the deeper meaning of phrases and entire queries
• NLP and semantic search prioritize user intent, aiming to deliver results based on what the user is actually looking for, while LSI is limited to analyzing patterns in word usage across documents.
• NLP handles language variations and ambiguity much better than LSI, which means it can offer more accurate and relevant results

Search engines, including Google, rely on NLP to improve the relevance of search results. NLP analyzes the context behind queries, breaking down user language and recognizing intent. This makes it easier for search engines to deliver results that answer the query more precisely, regardless of how the query is phrased.

For instance, when you search for “best running shoes for flat feet,” NLP interprets this as a user wanting recommendations for running shoes that suit their specific condition. In contrast, LSI would only recognize the terms “running shoes” and “flat feet” as related, but wouldn’t fully understand that the user is looking for a product recommendation based on specific needs.

In short, while LSI helped early search engines find related terms, NLP powers modern search engines to provide more context-driven, relevant results by understanding both words and meaning in a query.

The role of semantic search in SEO today

While LSI itself is no longer relevant, its foundational idea of moving beyond keyword matching has left an impact. Semantic search has fundamentally transformed how search engines understand content and deliver relevant results. Unlike early search algorithms that relied heavily on keyword matching, modern search engines, particularly Google, focus on interpreting the meaning, context, and intent behind a query. 

This shift allows search engines to provide results that are more accurate and more aligned with what the user is truly looking for, even if their query doesn’t contain the exact keywords in the most relevant content.

How semantic search works

Semantic search leverages advanced natural language processing, machine learning, and entity recognition to understand the relationships between words, synonyms, and related concepts. Instead of simply counting keyword frequency, search engines now analyze:

• Context: How a term is used in relation to other terms within a document or query
• Synonyms and variants: Understanding that different words can represent the same concept (e.g., “car” and “automobile”)
• User intent: Recognizing whether the user is looking for information, a product, or an answer to a specific question

These capabilities allow search engines to deliver more contextually relevant results by interpreting the user’s query and matching it to the most useful content, even if the phrasing is different from what the user typed.

In the past, search engines operated on simple keyword-based matching. For example, if someone searched for “best gaming laptops,” the search engine would return pages that contained this exact keyword phrase. However, this often led to poor results when users didn’t know the exact terminology, or when websites gamed the system by overstuffing keywords into content.

With semantic search, search engines now understand the broader context and intent behind such queries. For instance, a search for “best gaming laptops” today could yield results for “top-rated gaming laptops,” “gaming laptops reviews,” or even “features to consider in a gaming laptop.” The search engine understands the user is looking for information about top-performing devices, not just a page with the words “best” and “gaming laptop” repeated multiple times.

SEO today is less about stuffing content with related terms and more about ensuring that content is contextually relevant and answers the user’s intent. Semantic search helps search engines determine whether a user wants to buy a product, find information, or learn how to do something. This means that content must be optimized for specific keywords and how well it answers user needs. 

By understanding user intent, search engines can filter out irrelevant results, such as pages that might use the right keywords but don’t provide the information the user seeks. Search engines analyze content holistically, considering not just keywords but how well the content covers a topic, whether it addresses related subtopics, and whether it matches the user’s underlying intent. The goal is to provide comprehensive and useful results rather than those that merely contain the right keyword combinations.

Today, semantic search allows Google to deliver results that cover broader, related topics such as soil recommendations, watering schedules, and common pests for container gardening. Even if a webpage doesn’t include the exact query phrasing, it can still rank highly if it addresses these semantically related aspects​.

While using semantically related terms is still valuable for enhancing content depth and topic coverage, it’s not because of LSI. Instead, it’s due to the evolution of search algorithms that focus on understanding the meaning, context, and intent behind content. 

Therefore, successful SEO strategies should prioritize creating high-quality, informative content that addresses user needs comprehensively using highly valuable keywords and topic clusters rather than chasing the outdated idea of LSI keywords.

Common mistakes to avoid when using semantic search concepts

Although latent semantic indexing was once an influential concept in information retrieval, many need to be made aware of its relevance to modern SEO. As search engine algorithms have evolved, it’s crucial to avoid outdated practices and common pitfalls when optimizing content for semantic search. 

Below are key mistakes to avoid:

1. Over-optimizing with irrelevant keywords

Many believe stuffing a page with related keywords (synonyms or similar terms) will enhance semantic search. However, keyword stuffing can harm user experience and may even result in penalties from search engines. The focus should always be on context and meaning rather than keyword frequency.

2. Ignoring user intent

Semantic search prioritizes understanding the user’s intent behind the query rather than just matching keywords. Even if the right semantic keywords are used, content that doesn’t align with what users are actually searching for will not perform well.

3. Failing to address topic depth

Semantic search algorithms favor content that covers a topic in depth and breadth. Simply scattering related terms around the content won’t be enough. Search engines assess how well you address the various aspects of a subject.

4. Misunderstanding keyword relationships

It’s easy to confuse LSI with related terms or synonyms and not realize that LSI is a mathematical technique that looks at word relationships in large collections of text. Modern semantic search doesn’t merely depend on related keywords, but on understanding the context in which these terms are used.

5. Focusing on keywords over topics

Many people still approach semantic search with a keyword-first mindset. Modern SEO focuses more on topics, meaning creating content that thoroughly covers a subject rather than just a list of keywords is essential.

6. Overlooking structured data

Structured data (such as schema markup) helps search engines better understand the context and relationships within your content. Many websites fail to use this tool, missing an opportunity to enhance semantic understanding.

How to optimize content using semantic strategies

Optimizing content for search engines goes beyond traditional keyword targeting. It requires an understanding of semantic relevance and user intent. To succeed in this environment, your content must be contextually rich, cover related subtopics, and provide answers to the actual questions your audience is asking. 

Here are a few practical strategies for optimizing your content using semantic search principles.

1. Prioritize user intent

One of the most important aspects of semantic optimization is ensuring that your content aligns with user intent. Every query has an intent and understanding this intent helps you craft content that directly addresses what users are looking for. 

For example:

• Informational intent: If users search for “how to grow basil,” they likely want a detailed guide. Here, focus on step-by-step instructions, tips, and related subtopics such as “best soil for basil” and “watering schedules.”
• Transactional intent: If users search for “buy running shoes,” they’re likely ready to purchase. Content optimized for transactional queries should provide product comparisons, links to purchases, and customer reviews​.

Action steps:

• Identify the search intent behind the keywords you target
• Tailor your content to address that intent directly

2. Focus on contextually-relevant content

Search engines like Google look for more than simply a keyword’s frequency on a page—they seek to understand the overall meaning of your content. Therefore, your goal should be to create comprehensive, well-rounded content that thoroughly covers the topic at hand. 

This means:

• Addressing subtopics: Cover various facets of the primary subject. For example, if your article is about “best running shoes,” you might also include sections on “shoe materials,” “running surfaces,” and “foot types” to give the reader a more complete understanding.
• Answering questions: Tools like Google’s People Also Ask feature or FAQ schema can help you uncover common questions related to your topic. By addressing these questions directly in your content, you enhance your chances of meeting the user’s specific needs and ranking higher in search results​.

Action steps:

• Research all aspects of the topic you’re writing about
• Answer multiple related questions within the content and provide detailed explanations
• Use subheadings, bullet points, and tables to organize content in a clear, structured manner.

3. Target related concepts and synonyms

Rather than overloading your content with a single keyword, incorporate related terms, synonyms, and conceptually relevant phrases. 

For example, if your main keyword is “digital marketing,” include related terms such as “online advertising,” “SEO strategies,” and “social media marketing” to signal to search engines that your content is comprehensive.

One useful tool for this is Semrush’s Keyword Magic Tool, which helps you discover a broad range of related terms and phrases. By using this tool, you can generate a list of relevant keywords and topics that align with your primary focus. 

These suggestions will not only increase the depth of your content, they’ll also improve its semantic relevance, helping search engines understand that your article addresses multiple aspects of the subject​.

Action steps:

• Use keyword tools like Semrush’s Keyword Magic Tool to find conceptually relevant keywords and related phrases
• Integrate these naturally into your content, ensuring they contribute to its quality

4. Utilize structured data

Structured data (like schema markup) helps search engines understand the relationships between elements in your content. It enhances search engines’ ability to interpret your content and may even provide opportunities for rich snippets, which can improve click-through rates.

Action steps:

• Add schema markup to your content for things like FAQs, how-to guides, reviews, and products
• Use Google’s Structured Data Markup Helper to add the appropriate schema tags to your content

Well-structured content that answers user queries succinctly and thoroughly has a higher chance of being featured in Google’s rich snippets, People Also Ask sections, or as part of Featured Snippets.

5. Leverage natural language processing 

Search engines use NLP to understand how language works, enabling them to identify meaning and context from content. When optimizing content, it’s important to write in a natural, conversational tone that mimics how users search for information.

Action steps:

• Write for humans first, search engines second—this ensures your content is easy to understand
• Use long-tail keywords and question-based phrases to align with how users search

6. Build content that encourages engagement

Search engines monitor how users interact with content. Pages that engage users tend to rank better. Providing valuable, engaging content that holds the user’s attention will improve metrics like time on page and bounce rate.

Action steps:

• Use visual aids (images, videos, infographics) to make content more engaging
• Create interactive elements like quizzes, calculators, or surveys when appropriate
• Optimize for mobile devices, ensuring a smooth user experience

7. Use internal and external linking strategically

Internal linking helps establish your website’s structure and allows search engines to understand your content’s relationships better. External linking, on the other hand, signals trustworthiness by citing reputable sources.

Action steps:

• Link relevant internal pages to each other to create a strong, topic-based architecture
• Include links to authoritative external sources that validate your content
• Avoid overloading the page with links—keep it natural and focused on value

Tools for semantic optimization

Semantic optimization requires creating content that’s comprehensive, contextually relevant, and aligned with user intent. To help ensure that your content covers the depth of a topic and satisfies semantic search principles, several tools can assist in keyword research, content generation, and optimization. 

Here are some of the best tools for semantic optimization:

1. Semrush SEO Writing Assistant

Semrush’s SEO Writing Assistant provides real-time suggestions to improve the quality and relevance of your content. It analyzes the semantic richness of your text, recommending related keywords and phrases to ensure that your content is well-rounded and optimized for semantic search. 

Features include:

• Readability checks: Ensures your content is easily readable and understandable
• Targeted keyword suggestions: Based on the main topic, it provides related keywords that enhance semantic relevance
• Tone of voice: Adjusts the tone of your content to align with the audience’s expectations
• Plagiarism checker: Ensures originality, which is crucial for SEO success​

CTA: Try Semrush’s SEO Writing Assistant

2. Google Natural Language API

Google’s Natural Language API is a powerful tool that provides insights into the syntax and semantics of your content. 

It uses machine learning models to analyze text and offers features like:

• Entity analysis: Identifies key concepts and relationships between terms, helping to enhance the semantic depth of your content
• Sentiment analysis: Helps evaluate the tone of your content to ensure it resonates with your target audience
• Content categorization: Automatically categorizes text into topics, which can help align content with specific user queries​

3. ClearScope

ClearScope helps you create semantically optimized content by analyzing the top results for your target keyword and generating a list of related terms and topics. 

Features include:

• Content grading: Provides a score for your content based on how well it covers related concepts and keywords
• Topic coverage: Suggests additional subtopics that are related to your main keyword, ensuring that your content is comprehensive and well-rounded
• Competitor analysis: Helps you understand the semantic strategies used by top-ranking competitors, so you can enhance your content accordingly

4. Frase.io

Frase.io is an AI-driven content optimization tool that focuses on answering user questions and addressing user intent. 

It helps create content that is both contextually relevant and comprehensive by:

• Content brief generation: Automatically generates content briefs based on search intent and top-ranking pages
• Answer optimization: Helps structure your content to answer user questions effectively, increasing the likelihood of ranking in featured snippets
• Topic research: Identifies related questions and topics that should be covered to make your content more in-depth and relevant to user queries​

5. MarketMuse

MarketMuse is a semantic content optimization tool that uses AI to help you plan, research, and create high-quality content. 

It offers:

• Content gap analysis: Identifies areas where your content may be lacking in coverage compared to top-ranking pages
• Topic modeling: Suggests related topics and subtopics that can help improve the depth and breadth of your content
• Optimization score: Provides real-time feedback on how well your content aligns with semantic search principles, ensuring it’s optimized for ranking​

Take action to improve your SEO strategy

If you want your SEO strategy to succeed today, focus on creating high-quality content that comprehensively answers users’ needs. Forget chasing outdated ideas like LSI keywords—your priority should be understanding and addressing user intent while covering topics thoroughly.