Structured Data and Entity SEO

Search engines have always faced the same fundamental problem: HTML is designed for humans, not machines. A paragraph that reads "Dr. Sarah Chen, cardiologist at Stanford Medical Center, published a study on LDL cholesterol" contains rich information, but a crawler must infer what "Dr. Sarah Chen" is, what "cardiologist" means in relation to her, and whether "Stanford Medical Center" is the same institution mentioned elsewhere on the web. For two decades, search engines solved this with keyword statistics. That era is over.

Google's Knowledge Graph — a structured database of 500 billion facts about 5 billion entities — represents the alternative: a semantic layer where "Sarah Chen" is not a string of characters but a thing with properties, relationships, and a verified identity. When your content participates in that layer through structured data, you stop being a document to be ranked and start being an entity to be understood.

This chapter covers the three technical disciplines that together determine whether search engines and AI systems treat your content as authoritative structured knowledge or just another HTML file.

JSON-LD Schema Markup is the implementation layer — the <script type="application/ld+json"> blocks that make your entities, relationships, and content properties machine-readable. JSON-LD is Google's sole recommended format, and the data is unambiguous: pages with schema markup are cited in Google AI Overviews at 3.1× the rate of unstructured pages. Section 4.1 covers every high-impact schema type, the syntax patterns that power validation, and the validation tools that catch silent errors before they suppress rich results.

Entity SEO is the strategy layer — deliberately constructing your web presence so that search engines can identify and connect your entities (your brand, your authors, your topics) to the broader knowledge graph. Section 4.2 explains the sameAs property, topical authority architecture, and the signals that trigger Knowledge Panel creation.

E-E-A-T — Experience, Expertise, Authoritativeness, Trustworthiness — is the quality layer. It is not a single ranking signal but a multi-dimensional framework that Google's algorithms are trained to recognize through dozens of on-page and off-page signals. Section 4.3 translates each pillar into specific technical implementations: author schema, credential markup, and the structural choices that survived Google's December 2025 Core Update, which handed 2–5 position gains to sites with verified expertise while dropping content farms by up to 6.2 positions.

Together, these three disciplines form the foundation for both traditional rich-result visibility and the newer challenge of AI citation eligibility. An LLM summarizing search results needs confident semantic signals just as much as it needs good prose. By the end of this chapter, you will have the concrete implementation patterns to provide both.