From Schema Markup to Strategy: Unlocking the Full Value of Knowledge Graphs for SEO and AI

Martha van Berkel at BrightonSEO: From Tactic to Data Architecture

At BrightonSEO, Martha van Berkel, CEO and co-founder of Schema App, challenged marketers to rethink their approach to Schema Markup. Too often viewed as a technical SEO tactic for rich results, she reframed Schema Markup as the foundation of a content knowledge graph—a data architecture that future-proofs organizations for both search and AI.

To open her talk, Martha shared her own personal knowledge graph—a map that connected her career at Cisco, her time at MIT, her Canadian roots, and even a fun fact about her Austin Healy Sprite once driven by Kevin Bacon. The audience laughed as she pointed out how, just like in the “six degrees of Kevin Bacon,” knowledge graphs let us infer relationships. That ability to infer meaning, she explained, is what both search engines and AI models do at scale.

Why Schema Markup Matters Now

Martha highlighted how 2024 marked a turning point. Google and Microsoft both emphasized structured data in their AI documentation, with Bing even leveraging schema markup in IndexNow to track product changes. Studies are now showing that schema-enriched content is 30% more likely to be sourced by AI platforms, underscoring its growing strategic role.

Beyond SEO: The Three Layers of Value

She explained how Schema Markup creates value in three expanding layers:

• SEO – Rich results, higher click-through rates, AI Overviews
• Content Insights – Mapping entities reveals topic authority and gaps, guiding content strategy.
• AI Readiness – A content knowledge graph becomes a reusable data layer that feeds into agents, copilots, and enterprise AI initiatives.

Schema Markup, when linked through internal and external entities, evolves from markup into a knowledge graph. That shift allows marketing teams to unlock more than visibility—it positions them as data architects supporting the entire enterprise.

From Rich Results to Enterprise AI

Martha emphasized that building this data layer requires depth, breadth, and ongoing management. She shared case studies, like Brightview Senior Living, where robust entity linking drove a 25% increase in non-branded clicks. She also pointed to emerging applications—Schema App’s own MCP server integration with copilots and Vertex AI, and Microsoft’s NLWeb project—that show how Schema Markup is already bridging into enterprise AI.

The Takeaway

Her message was clear: Schema Markup is not just for SEO—it’s the connective tissue of a smarter marketing ecosystem. By building content knowledge graphs, organizations unlock visibility, insight, and innovation, making their data not just machine-readable but machine-understandable.