What Are Semantic Keywords?
Semantic keywords are words and phrases that are topically related to your main subject. If your page is about "running shoes," terms like trainers, sneakers, cushioning, gait analysis, marathon, and pronation are semantic keywords. They signal to search engines and AI engines that your page covers the topic with real depth instead of just repeating one phrase. An Ahrefs study found the average top-ranking page also ranks for around 1,000 related keywords ā because it covers the topic, not just one keyword.
You may see the term "LSI keywords" used interchangeably. LSI stands for Latent Semantic Indexing, a 1988 algorithm from Bell Labs (Deerwester et al.). Google's John Mueller has publicly stated Google does not use that specific math ā modern engines use BERT, MUM, and word embeddings instead. But the underlying principle is identical: broad, semantically-related vocabulary signals topical relevance, and that is exactly what modern systems reward. This metric is part of the Content Quality pillar in your GEO-Score.
How Semantic Search Actually Works
Understanding why semantic keywords matter requires knowing how modern search engines process text. The technology has evolved dramatically from simple keyword matching.
1. Text becomes vectors
When you publish a page, search engines convert your text into numerical vectors (embeddings) using models like BERT. Each word and sentence gets mapped to a point in a high-dimensional space where similar meanings cluster together. "Running shoes" and "trainers" end up near each other; "running shoes" and "kitchen blender" do not.
2. Queries match by meaning, not letters
When a user asks ChatGPT or Google a question, that query also becomes a vector. The engine then finds pages whose vectors are closest to the query vector. Pages with rich semantic vocabulary produce vectors that sit near many different query vectors ā which means they surface for more queries than pages repeating a single phrase.
3. Entities connect to the Knowledge Graph
Since Google Hummingbird (2013) and BERT (2019), engines reason about entities ā people, products, concepts, places ā and the relationships between them. Bill Slawski's patent research showed Google uses Knowledge Graph entities and co-occurring terms to verify a page genuinely covers a topic. Named entities in your content trigger this recognition.