Latent Semantic Indexing [LSI]

Latent Semantic Indexing (LSI) is a mathematical technique originally developed in the late 1980s for information retrieval and natural language processing. It works by analysing the relationships between words and the documents in which they appear to identify patterns of meaning and to understand that different words can refer to the same concept and that the same word can mean different things in different contexts.

The “latent” in Latent Semantic Indexing refers to the hidden, underlying meaning that connects related terms, even when those exact terms do not appear together. For example, an LSI system would learn that “car,” “vehicle,” “automobile,” and “motor” are conceptually related, even if they never appear in the same document, because they consistently appear alongside similar sets of other words across a large body of text.

In SEO, LSI became a popular topic in the 2000s and 2010s, with many practitioners advocating the use of “LSI keywords”, which are semantically related terms included within content to help search engines better understand the topic of a page. Tools emerged that claimed to identify LSI keywords, and the concept was widely discussed as a way to move beyond exact keyword matching towards more natural, contextually rich writing.

However, it is important to note a significant caveat:

Google has repeatedly stated that it does not use Latent Semantic Indexing as part of its ranking systems. The term “LSI keywords” is therefore something of a misnomer in the SEO industry. The concept of using related, semantically relevant terms to enrich content and signal topical relevance is sound, but it is not accurately described as LSI, and the specific techniques and tools marketed under that label should be treated with scepticism.

Google uses far more sophisticated natural language processing systems, including neural networks and models such as BERT and MUM, to understand the meaning and context of content, which go well beyond what LSI was originally designed to do.

While Latent Semantic Indexing itself is not a mechanism used by Google, the underlying principle it represents (that search engines seek to understand the meaning and context of content rather than simply matching keywords) is highly relevant to modern SEO. Writing content that naturally incorporates related terms, covers a topic comprehensively, and uses language in a contextually appropriate way remains important for helping search engines understand what a page is about and assess its relevance to a given query.

The key takeaway for SEO practitioners is to focus on producing genuinely thorough, well-written content that covers a subject in depth, rather than mechanically inserting lists of “LSI keywords” generated by tools that may not deliver on their promises.