What is Latent Semantic Indexing?

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What is Latent Semantic Indexing?

If you work in the SEO field, it’s likely you’ve heard the term “latent semantic indexing”. If you haven’t heard of it, there are some that claim it is key for overall organic search success. Jumping on the web will lead you to some well-known influencers in the industry, like HubSpot, advising that injecting just a bit of latent semantic indexing, or LSI, will bring your SEO game to the next level. But, even with all the praise, you still might find yourself wondering just what LSI is or if there is any available evidence to prove it actually boosts your SEO efforts. So, we’re here to plainly explain what you need to know and suggest a few impactful LSI strategies.

What is Latent Semantic Indexing?

LSI is a mathematical method developed in the late 1980s with the purpose of improving the overall accuracy of information retrieval. Utilizing a technique called “singular value decomposition”; LSI scans unstructured data in documents and then identifies the relationships between concepts contained within said documents. In short, latent semantic indexing finds the hidden, or latent, relationships between words, or semantics, in order to improve understanding, which is indexing.

The application of LSI allowed those in the field of text comprehension to take a big leap forward, as LSI accounted for the contextual nature of language. Earlier attempts saw technology struggle both with synonyms that were part of the use of natural language and with meaning changes that come with new surroundings. Latent semantic indexing is capable of determining where terms and concepts fall and tends to work best on static content and small sets of documents. With LSI, documents can be grouped together based on their common themes. The latter was incredibly useful for early search engines.


Since latent semantic indexing can help search engines understand synonyms, it could very well help a search engine understand your content, index said content, and then help it rank for target keywords and questions. In fact, some use the term “LSI keywords” to describe keyword synonyms. This is all well and good, but there is no evidence to support this notion. Latent semantic indexing may have played a role in helping early search engines adapt, but the game has changed significantly since the late 1980s. These days, there is much more advanced machine learning technology for document indexation and information retrieval.

With a resurgence of focus on latent semantic indexing, it’s important to note that one’s time may be better spent understanding the true function of semantic search instead of focusing on sprinkling content related synonyms throughout your work. Using structured data to your advantage and understanding how it benefits content indexing is much more valuable than simply adding LSI terms.

Scott Freeman
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