What are semantic keywords? Here's how to find & use them
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Marketing Hub Marketing automation software Free and premium plans Service Hub Customer service software Free and premium plans Content Hub Content marketing software Free and premium plans Revenue Hub CPQ, billing, and payments software Free and premium plans Agent Hub Your central home for building and managing AI agents across the platform Learn more AEO (Beta) Answer engine optimization tools that track and improve your brand's visibility in AI results Learn more Home Marketing What are semantic keywords? Here's how to find & use them What are semantic keywords? Here's how to find & use them Written by: Cassie Wilson Clark HUBSPOT'S FREE AEO GUIDE Navigate the AI revolution and with proven strategies to optimize your content for AI visibility. Every content marketer seems to be asking the same question: Do semantic keywords still matter in SEO in 2026, especially now that AI engines influence traffic and buying decisions? Google processes more than 5 trillion searches annually. But content marketers should pay closer attention to how Google interprets those queries. Its algorithm no longer evaluates pages by scanning for exact-match keyword strings. Like AI answer engines such as ChatGPT, Perplexity, and Gemini, it evaluates meaning. In 2026, brands need content that demonstrates deep topical understanding to rank in traditional search and earn citations in AI-generated answers. That means marketers should move beyond generic keyword lists and optimize content around relationships, entities, and the questions buyers actually ask. This guide walks through what semantic keywords actually are, how they differ from outdated LSI tactics, and outlines a repeatable, step-by-step process for finding and using them in 2026 — whether a brand is optimizing for Google, AI Overviews, or answer engines like ChatGPT. Semantic keywords are the related terms, concepts, and entities that help search engines and AI platforms understand what your content is actually about. They’re essential for both traditional SEO rankings and getting cited in AI-generated answers. Semantic keywords are terms that are semantically related to a page’s topic and keyword intent. They help search engines interpret context beyond exact-match phrases. Think of them as the words, phrases, and concepts that naturally surround a topic and signal the real subject of the content. For example, if the primary keyword is “email marketing software,” semantic keywords might include: Semantic keywords often include synonyms, modifiers, and related questions that a comprehensive piece on the topic would naturally cover. Navigate the AI revolution with proven strategies to optimize your content for AI visibility. I asked Kelvin Çobanaj, CEO of ZeroRank, why semantic keywords matter for SEO and AI search optimization. Çobanaj points to two reasons these high-intent keywords matter. First, he says, “With traditional SEO, semantic keywords are mostly variations of the same search so that a page can rank for more queries.” When Google encounters a piece of content that uses the right cluster of related terms, it gains greater confidence that the page genuinely covers the subject rather than merely mentioning a keyword in isolation. That confidence translates into better rankings and, increasingly, a better chance of being cited in AI-generated answers. The second reason? Semantic keywords support topical authority when used across a topic cluster to answer the questions buyers are asking. That helps brands build a connected set of content that both Google and AI engines can understand. Çobanaj says, “With AI search, I focus more on covering the full topic and common questions, not just keyword variants. That gives AI enough context to include the brand in its answer.” To clarify, LSI keywords are not the same as semantic keywords, and the term itself is outdated. LSI (Latent Semantic Indexing) refers to a mathematical technique introduced in a 1988 research paper that analyzes word co-occurrence patterns in documents. In plain terms, LSI looks at which words are most likely to appear together. Google’s own John Mueller confirmed on X in 2019 that Google does not use LSI. Modern search engines rely on far more sophisticated natural language processing (NLP), including transformer models like BERT and MUM, which understand language contextually in ways LSI never could. LSI tools often spit out loosely related terms based on statistical co-occurrence. Semantic keyword research, on the other hand, focuses on meaning: what concepts, entities, and questions does a searcher expect your content to address?
Source: HubSpot
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