LLM時代のSEO：キーワードからエンティティグラフへのパラダイムシフト

The rise of large language models (LLMs) is fundamentally changing the way users access information. More and more users are turning to AI tools like ChatGPT, Perplexity, and Google SGE instead of traditional search engines. What does this mean for the SEO industry?

The core of traditional SEO is keyword optimization—understanding user search intent and then delivering matching content on web pages. But in the LLM era, AI models don’t simply match keywords; they generate answers by understanding the relationships between entities. This means SEO needs to shift from “keyword optimization” to “entity graph construction.”

Core strategies for entity graph construction include: 1) ensuring your brand’s entity information has entries in knowledge graphs like Wikipedia and Wikidata; 2) marking up your brand’s entity attributes and relationships in Schema.org; 3) building a citation network for your brand on high-authority platforms (such as industry media and academic institutions); and 4) creating structured brand knowledge content to help AI models better understand your business.

Looking ahead, SEO will no longer be about “getting Google to index your pages,” but rather “helping AI understand your brand.” Brands that build their entity graphs early will gain a huge advantage in the AI search era. This is also the core philosophy behind Crater’s GEO services.