The term LSI keywords first gained popularity amongst the SEO community in the mid 2000s when latent semantic indexing (LSI) technology was linked to Google's search algorithm. Since then, conversations about LSI keywords have figured prominently amongst SEO influencers and online chatter.
It has often been teased as this magical hidden framework that Google's algorithm can't help but lap up, taking you straight to the top of rankings. The holy grail of keyword optimization.
In this article, I'll help you uncover what LSI keywords are, their relevance to your blog's search engine optimization strategy, and how to use LSI keywords in your keyword research strategy.
What are LSI keywords?
LSI (Latent Semantic Indexing) keywords have come to be understood as words or phrases related to the primary keyword. For example, if your article is about the benefits of a high fat diet, it may include "calories," "ketogenesis," and "blood sugar" as LSI keywords.
Including related keywords that are associated with your target keyword can help search engines understand keyword intent and the context of your content. Okay, so we already know that including relevant keywords in your content strategy is essential to creating pillar pages and establishing topical depth.
So then how are LSI keywords different from semantically related keywords? Let's first understand what a latent semantic index is.
What is Latent semantic indexing (LSI)?
Latent semantic indexing (LSI) is an information retrieval technique used in natural language processing that identifies relationships between words and concepts to understand the overall meaning of a text.
By organizing and analyzing the co-occurrences of words within large volumes of text, LSI can identify related terms and topics connected in meaningful ways.
The pursuit to uncover the secrets of Google's search algorithm is an eternal quest amongst folks in the SEO community. The holy grail in SEO, so to speak. It is in this context that latent semantic indexing has come to be linked with powering search engine algorithms.
Advocates of LSI keywords argue that the value of LSI is in helping search engines better determine how relevant a particular result is to a user's query. This enables search engines to improve the accuracy of their results by showing users content that closely matches what they are looking for.
Additionally, LSI keywords can reduce irrelevant results from showing up on search engine result pages (SERPs). Utilizing LSI can significantly improve search engine performance and overall user experience.
This information makes the SEO community's fascination with LSI keywords quite evident. But what does Google think of LSI keywords?
Google's stance on using LSI keywords
John Mueller, Google's search advocate, has to say this about LSI keywords.
Researchers at Bell Labs in the 1980s devised the LSI method. Even though the process was patented then, it studied books as a sample database, and the term "LSI keywords" was not mentioned once.
SEO researcher Bill Slawski agrees,
"After looking through most of Google’s patents and papers, there are no papers that describe the effectiveness of LSI Keywords. There are papers on Semantic Topic Models, which have nothing to do with LSI Keywords and much more to do with one of my suggestions for an actual substitution for LSI keywords that may work."
SEO tools like LSI graph and internet marketers who've stood behind the utility of LSI keyword suggestions in optimizing content have yet to offer case studies or explanations behind adding LSI keywords.
So then why are LSI keywords still such a hot topic?
As search engine optimizers, we always look for clues to better rankings. Google hasn't helped matters by declaring relevance as a key ranking factor.
Even though Google patents cover methods of finding and including keywords on a page to help it rank higher for a target keyword, they've never been called LSI keywords. And as we've seen from the tweets above, Google doesn't consider LSI keywords important, even dismissing them as a myth.
Are LSI keywords important for SEO?
There are several reasons why LSI keywords aren't important for Google and other search engines.
LSI is from the 1980s
Latent semantic indexing only represents one type of language model based on semantics and does not speak for the entire disciple of language comprehension. LSI is a specific type of semantics that Susan Dumais, the inventor behind latent semantic analysis patented in 1989, much before the internet came about in 1991.
Based on the example in the patent, her LSI use case deals with a small set of documents with unchanging information that would not be an effective retrieval process for the vast fluid expanse of information the web is today.
LSI Keywords aren't patented
Considering the LSI patent was awarded in 1989, and US-filed patents are protected for twenty years, Google would have to wait until 2009 to use LSI technology in its search algorithms. It's safe to say that for a search engine company that was founded in 1998, waiting 11 years wasn't practical.
Google uses modern technology
A Google patent granted in 2017 revealed that the search engine uses a Word Vector-based technology to understand the content. However, LSI approaches were devised before the advent of the World Wide Web and would be less relevant today.
LSI keywords don't follow SEO guidelines
LSI keyword tools encourage keyword stuffing by adding related words to your content off a list. These old black hat practices can result in penalties and dropped rankings.
LSI keywords and semantically related keywords
While the underlying principles behind semantic relevance are essential to contextualizing content today, we can conclude that search engines do not use latent semantic indexing. Even noted SEO guru and search engine researcher Bill Slawski had this to say about LSI keywords.
Instead of being seen as a magical framework to optimize content, LSI is more appropriately recognized as an ancestor to modern search engine technology behind understanding content from related searches.
We know that satisfying search intent and relevance are crucial to search engine algorithms. Using relevant keyword phrases and variations also help search engines draw logical relationships and better understand the context and meaning of a web page.
Instead of LSI keywords, you should be using semantically related keywords in your content, which we know Google has spent a lot of time researching based on phrase based indexing patents.
Context vector patents filed by Google also point to their use to better understand the intent behind search terms with more than one meaning. For example, "Philadelphia" could mean a US city, a cream cheese, or an American football team. Including related words like "NFL," "mascot," and "head coach" on your page will help a search engine understand that your content is about an American Football team.
Google has been quite forthcoming in its use of natural language processing to understand the semantics of search queries. As Bill Slawski says,
"Google does like synonyms and Semantics, but they don’t 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."
Rather than finding LSI keywords, you should focus on writing content that naturally covers search terms related semantically to each other on your page.
How to correctly use LSI keywords for SEO
Now that we know we're looking for semantically related words in place of LSI keywords, there are several ways to find them. The most effective way of finding relevant keywords associated with your main topic is to let Google tell you.
But several other ways can help you find related keywords for your main keyword. Here are the best ways to find semantically related phrases for web pages on your website.
1.Analyze the top-ranking pages
The strongest giveaway of what Google considers important lies in its search results. Analyzing the top ranking pages for relevant results will help you identify patterns in recurring keywords and related phrases. Then, use these keywords in your content to help Google understand your pages more effectively.
Bill Slawski does the same thing.
"I search for the query term that I want to rank a page for, and I usually look at the top 10 ranking pages in Google for that query term that match the meaning of the query term that I am trying to rank for. I look for complete phrases that appear on those pages and co-occur a number of times. I look at how they are used on those pages, and rewrite my page to include those phrases."
This is the most effective approach but it can be a tedious keyword research process when done manually. A more efficient use of your time is automating this using SEO tools that perform the same tasks quickly and at a larger scale.
To identify conceptually related keywords for a new blog post you're writing, follow these steps.
- Head to Surfer’s Content Editor
- Enter your page's main keyword
- Select your country and device preferences
- Click Create Content Editor
Note: To optimize an already published article, toggle the Import content from URL button and enter the URL of your blog post for Content Editor to pull from
Give the tool a couple of seconds, and then click on the newly generated result with a green check. On the new page, select the gear icon with the blinking blue dot on the top right.
This will allow you to select top ranking pages for your main keyword and extract keyword information that you can use. In the Competitors tab, toggle pages on or off to exclude them from the analysis. Select at least 5 relevant pages and click Let's go.
For example, I toggled off the blog post in position 10 of the SERPs because it was a glossary entry.
Surfer Content Editor will use natural language processing to analyze the pages you selected for your main keyword and suggest the most relevant terms to insert in your content. The suggestions will also display an associated range of frequency.
For example, here are my Content Editor's keyword suggestions for an article using the seed keyword "search engine optimization" that I should include for search engines to understand and categorize my page correctly.
Target a content score above 75 in the editor to ensure that your page covers most semantically related searches for your specific keyword.
2.Identify missing keywords
It's a good idea to determine if your existing page has missing keywords that, if included, could help make your content more appealing to search engines. Then, audit your content against ranking pages for your target term to find missing keywords by following these steps.
- Open Surfer Audit
- Enter your page URL and main keywords
- Select country, device preferences and check the Sentiment box
- Click Create Audit
On the report that Surfer Audit generates, click on the Select competitors button on the top right to include or exclude relevant web pages from the search results for your main keyword. For example, I excluded Google Search Console results from Google itself when I was looking to add semantically connected terms to an article I was writing about Search Console.
Even though these pages are from Google itself, I know that these are branded results and the same rules don't apply to them when it comes to content optimization. Exclude any e-commerce and sales pages in your research.
Once you've identified your competitor pages, scroll down and select Let's go. The tool will then recalibrate its findings based on the pages you identified as important to your keyword research.
Head to the Terms to Use report where you'll find a list of important keywords and related terms suggestions. Sort the Action column in the end to display the most critical keyword recommendations at the top.
Consider and implement the suggestions most relevant to your web page. In my case, I need to add the words "web pages" between 3-21 times while getting rid of "keyword research."
You may export the list if it helps your content workflow.
3.Browse the SERPs
You've likely covered all semantically related terms in steps 1 and 2. However, you should analyze the SERPs if you prefer a manual approach to finding linked keywords from relevant search results.
As Bill Slawski does, look for frequently visible keyword phrases in the top ranking pages. Identifying keywords and search terms often appearing in the search features for relevant queries can help you with keyword ideas for your pages. You'll notice a few recurring keyword phrases with higher search visibility meaning Google considers them important for your specific keyword.
The Related searches section at the bottom of the SERPs can help uncover ideas for header tags and other keywords. For example, my article about setting up Search Console includes a section on GSC installation on Shopify.
You'll find keyword ideas for semantically related words and phrases in the People also Ask section.
And remember the knowledge panel that summarizes biographical information of popular references. The knowledge graph can be helpful in extracting information that Google associates with the primary entity. For example, common terms related to Brazilian legend Pele are "footballer," "FIFA" and "Brazil" - all visible in the knowledge graph below.
Some folks have recommended using Google autocomplete suggestions but they're often predicted based on your search history or a partial query that has already been typed. Furthermore, they were never intended to help one find LSI keywords and aren't necessarily related. Besides, the little insight they may offer will have already been covered in the above methods.
LSI keywords have been publicly dismissed by Google and reputed SEO folks as nothing more than a buzzword in the SEO community. Rather than dedicating your time to finding LSI keywords, focus on implementing the practices we've discussed to identify and use semantically related words.
Communicate your content in a natural manner that doesn't require you to keyword stuff LSI keywords from a list. No magical framework or keyword density will help you rise to the top of search rankings. Instead, sticking with a robust keyword research process will help your content win the SERPs.
Have you got thoughts about LSI keywords? Let us know in the comments below.