What is LSI?
Latent Semantic Indexing or latent semantic analysis refers to a technique developed in 1980s for natural language processing that is aimed at identifying relationships between the terms contained in various documents to produce sets of concepts for these terms.
What Is the Meaning of ‘LSI Keywords’ in SEO?
There are plenty of influencers, like Hubspot, across the web who claim that including LSI keywords in your keyword research and implementing in your onsite optimization can deliver a lot of benefits to your rankings. Can it really?
Before we decide whether ‘LSI keywords’ can boost your SEO efforts, it is important to first understand what SEO specialists refer to.
When talking about Search Engine Optimization, Latent Semantic Indexing is referred to vocabulary terms, or synonyms, of the words and phrases the content or page is optimized for. When indexing the page, Google looks for the context of the page in order to interpret its purpose and deliver the most relevant search results. And it does not only refer to using the synonyms, it rather refers to using strategic keywords together.
For instance when you are doing a keyword research for ‘SEO Services’ and optimizing your page, think of any other keywords that you would like to include in your page. These keywords might not even be synonyms, meaning that one word might not ‘replace’ the other however we know that these keywords are likely to resonate with the ‘intent’ of the person searching for the certain service.
Good example: when you are searching for ‘institutions in Toronto’, Google comes back with a variety of schools, educational websites, universities some of which do not even contain the word ‘institutions’. Google simply interprets the intent – it assumes I am looking for educational institutions based in Toronto.
And that is what the search engine optimization specialists refer to when talking about LSI keywords: using the variation of keywords for context vocabulary. In their paper published in 2016 Google refers to this approach as “Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering”.