You’re moving beyond just linking organizations and are instead building a cohesive entity graph. By using Person schema to connect the key individuals (Dixon Jones, Genie Jones) to both the established “Inlinks” brand and the new “Waikay” venture, you are providing explicit, machine-readable signals about the relationship between them. This is precisely how you can help AI systems and search engines “connect the dots.”

Why Your Approach Is Correct

Your strategy works because it’s based on establishing shared entities. When AI scrapers or Google’s crawlers encounter schema on both domains that references the same individuals, it reinforces the connection between the two organizations. * Entity Association: You are explicitly stating that the same trusted person/entity (Dixon Jones) is the owner of both the established organization and the new one. This is a very strong signal. * Trust by Proxy: The established trust and authority of “Inlinks” and Dixon Jones can be passed to “Waikay” through this shared entity association. It’s not a backlink, but it functions similarly by transferring a form of authority. * Knowledge Graph Construction: You are essentially helping Google and other AI systems build out their Knowledge Graph. This can lead to better brand recognition, richer search results (like knowledge panels), and a deeper understanding of who is behind your new brand.

How to Implement This Correctly

To maximize the effect, you should use a consistent and interconnected schema strategy across both domains. 1. Create a Unique URI for Each Person: To ensure machines know it’s the same person, you should ideally have a unique identifier for Dixon Jones and Genie Jones. This is often an “About Me” page URL or a social media profile URL that you can reference using the @id or sameAs property. 2. Use Appropriate Linking Properties: * For the Organization schema on both Inlinks and Waikay, you can use properties like owner, founder, or member to link to the Person schema. * For the Person schema for Dixon and Genie, you can use alumniOf (if they founded/previously worked there), worksFor, or affiliation to point to both organizations.

Example Snippet for Waikay

JSON

{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Waikay",
  "url": "https://www.waikay.com",
  "founder": {
    "@type": "Person",
    "name": "Dixon Jones",
    "sameAs": [
      "https://www.inlinks.com/about/dixon-jones", // Example unique URL
      "https://www.linkedin.com/in/dixonjones/"
    ]
  },
  "member": {
    "@type": "Person",
    "name": "Genie Jones",
    "sameAs": "https://www.linkedin.com/in/geniejones/" // Example unique URL
  }
}

By nesting the Person schema within the Organization schema and using sameAs to point to authoritative profiles, you create an unambiguous and powerful connection. You’re not just telling search engines that Waikay exists; you’re telling them who is behind it and what trusted entities they are associated with. Your theory is spot-on and demonstrates an advanced understanding of how semantic SEO works.

Building Semantic Fingerprints: How I Connected Two Organizations Through Schema

Interactive artifact 

I’ve created a comprehensive case study article that retains the technical depth while presenting it as your first-person experience. The article maintains the sophisticated understanding of semantic SEO you demonstrated, includes only one set of bullet points as requested, and structures the information in a way that’s both informative and easy to grasp.

The piece covers your strategic thinking, implementation details, the schema structure you used, and the broader implications of building semantic fingerprints between organizations. It’s written in that authoritative, experienced tone that matches your background while remaining accessible to others who want to understand and implement similar approaches.