Latent Semantic Indexing Analysis by Search Engine Mentor

Latent semantic indexing (LSI) or latent semantic analysis is a method used to analyze a collection of documents to display word statistics together which then provide insights about the words and documents.

LSI uses mathematical techniques to find semantically related terms in a collection of words (an index) where it might be hidden.

In the context above, it looks like this LSI has a very important role in SEO (search engine optimization), right?

After all, Google itself is a huge “information library”, and until now we have heard a lot about semantic search and how important relevance is in search ranking algorithms. So, is it possible that this LSI can be a factor affecting ranking? Let’s peel the facts.

Actually, the claim is quite simple. Optimizing web content using LSI keywords will help Google understand the content so that your website gets a higher ranking. By using contextually related terms, you can deepen Google’s understanding of the content that friends create.

Google relies on LSI keywords to understand deep content. LSI keywords are not synonyms. However, terms that are close to the keywords you are targeting. Google does not just use the exact same terms when you do a search. Google also uses words and phrases that look similar.

Therefore, you have to really pay attention to the words and phrases you want to include in the content. Google also explains that the “simplest ranking signal” from relevance is the keywords used by users appearing in your content. If you don’t include the keywords users are looking for, then how will Google understand that your content is the best?

Keyword matching was used at IR at the time, but its limitations were evident long before Google existed. Until now, people too often search using words that don’t exactly match the words used in the index, this happens for 2 reasons:

  • Synonymy: Multiple words used to describe a single object or idea that elicits relevant results that are neglected.
  • Polysemy: Different meanings of a single word displaying irrelevant results are fetched.

Both of the above are still a problem today.

However, the methodologies and technologies that Google uses to improve relevance have long since moved away from LSI.

What LSI does is automatically create a “semantic space” for information retrieval (IR).

And as explained in the patent, LSI treats this unreliable data as a statistical matter. Without much trouble, the researchers actually believed that there was a hidden underlying semantic latent structure that they could remove from the word usage data.

Doing so uncovers latent meanings and allows the system to return more relevant results (only the most relevant ones) even if there is no eFrom the image above we can see that there are 2 separate steps that occur:

First, the data set or index undergoes Latent Semantic Analysis (LSA). Second, the query is analyzed and the indexes that have been processed are then searched for similarities.

Even though it’s a little difficult for us to understand, that’s the fundamental problem related to the LSI myth as a Google ranking factor.xact keyword match in them.

Pretty reasonable right? That’s why some people believe that this LSI is part of the ranking factor. If only using keywords is considered a relevance signal, then let alone using LSI keywords, surely the signal will be stronger.

Actually there is quite a bit of suspicion in some views of SEO people regarding Google who might say “misleading” things to maintain the integrity of its algorithm. But, is it really like that? Let’s review further.

First, it’s important to understand what an LSI is and where it came from. Latent semantic structure (LSS) first emerged as a methodology for retrieving textual objects from files stored in computer systems in the late 80’s.

Thus, this LSS becomes an example of one of the information retrieval – IR (information retrieval) concepts available to programmers. Then, together with the increasing storage capacity of computers and the availability of electronic data sets which are also increasing, the search for information is becoming increasingly difficult.

Google’s index is very large, it contains billions of web pages, and continues to increase over time. Every time a user types a query, Google sorts its index quickly to find the best answer.

Shailendra Kumar

DA50+Guest Post sites