Entity-Based SEO

The relevance of entities in search engine optimization has significantly increased with the introduction of generative SERPs – as implemented in Google Search Generative Experience (SGE) – and the intensified use of large language models (LLMs). Classic keyword rankings are progressively receding as search engines and AI systems are now capable of comprehending and evaluating complex relationships between entities. This necessitates a more precise and holistic information architecture. Optimization is based on the coherence and interconnectedness of entities within knowledge graphs to enable a semantically deeper search beyond traditional, string-based queries. The shift to entity-centric indexing is evidenced by a doubling of relevant entities per search query within three years.

Brands without clean entity hygiene disappear in answer engines regardless of keyword rankings.

A specialized author meticulously curates their Wikidata profile, including detailed sameAs links to VIAF and GND, a complete ORCID ID, and an accurate list of all scientific publications. Through this semantic enrichment using machine-readable identifiers, not only does visibility in academic databases increase, but the citation rate of their works within the responses of Large Language Models such as GPT-4 and Gemini also rises by a significant 40 percent within one year. This results in enhanced authority within knowledge graphs.

Entity-Based SEO gained significant traction from 2024 onwards, as search engines like Google increasingly oriented their algorithms towards entities and their interconnections within knowledge graphs. This shift was driven by the maturing of Large Language Models (LLMs), enabling more precise semantic analysis and improved contextualization of information. By 2026, it became a standard for advanced SEO strategies, with agencies like Davies Meyer recognizing the benefits of deeper semantic understanding of search intent.