Semantic SEO is the craft of modeling how a search engine understands content at a deeper level and writing in a way that aligns with that understanding. For many years the SEO industry promoted the idea of so-called LSI keywords and built entire tool categories around that name, selling lists of related words as if they were a hidden ranking factor. The reality is that Google has never used the Latent Semantic Indexing algorithm — it is a mathematical method from the late 1980s that simply cannot scale to the size of the modern web. The understanding models that power Google today are built on a fundamentally different foundation and rely on transformer-based neural networks trained on enormous corpora of natural language.
What LSI really was and why it has nothing to do with Google
Latent Semantic Indexing was developed in 1988 at Bell Communications Research as a way to surface hidden relationships between words in small collections of documents. The technique relies on singular value decomposition, a heavy matrix operation that was already computationally demanding for collections of a few thousand texts. Google indexes trillions of pages, and applying LSI at that scale would be impractical even with today's supercomputing resources. Official Google representatives, including Gary Illyes, have repeatedly confirmed that the company has never used LSI and that the phrase LSI keywords has no algorithmic basis whatsoever.
How the LSI keywords myth took hold
The persistence of this misconception comes from the SEO community's desire for scientific-sounding explanations of the Google black box. Tool vendors filled that vacuum by rebranding ordinary related-word generators as LSI keyword tools, even though those tools simply surface terms that frequently co-occur within a topic and have no connection to the actual mathematics of LSI or to how search ranking really works.
What Google actually uses
In 2019 Google rolled out BERT across its search systems, calling it one of the biggest changes of the past five years. BERT stands for Bidirectional Encoder Representations from Transformers and the model understands words not in isolation but in the full context of the sentence, looking at relationships in both directions at once. In 2021 Google introduced MUM, which the company described as a thousand times more powerful than BERT and capable of analyzing text, images and video together across many languages. Since 2024 Google has been integrating its Gemini family of models into core search infrastructure, powering the generative search experience and significantly expanding the system's ability to interpret user intent.
Entity-based search and the Things not strings philosophy
The launch of the Knowledge Graph in 2012 moved search from the level of strings to the level of real-world entities. Google captured this shift in its official slogan Things not strings, which means the system treats a term like Tashkent not as a sequence of letters but as a specific city with a population, coordinates, history and connections to other places. When your content references entities that exist in Wikipedia, Wikidata or other open knowledge bases, Google can link your text to the knowledge graph and increase its confidence in your topical authority.
How to write semantically rich content
The guiding principle of modern semantic SEO is to cover a topic completely and to answer the full range of related questions a reader might have within a single piece. If you write about building a website, it is not enough to describe the technical process alone; you also need to address domain selection, hosting choices, design principles, content strategy and basic search optimization in a natural flow. This approach is known as topical authority and it shapes your site's reputation as a trustworthy source within its field, while ideas like co-occurrence and co-citation reinforce that signal by showing Google that you discuss the concepts that genuinely belong together inside the topic.