Develop a systematic framework for evaluating brand authority and entity strength within the Knowledge Graph to improve AI-driven visibility and trust signals.
15 min read
Foundations
Visual diagram
A relational map showing a central 'Brand Entity' node connected via 'sameAs' and 'kbity' links to Wikidata, LinkedIn, industry journals, and the Google Knowledge Graph.
Section 1 of 10
Introduction
In the era of Generative Engine Optimisation (GEO) and AI-led search, visibility is no longer solely about keyword placements or backlink counts. Instead, Large Language Models (LLMs) and search engines prioritise entities—unambiguous, uniquely identifiable nodes of information. Auditing authority and entity signals is the process of assessing how well an AI understands who you are, what you do, and whether you can be trusted. This lesson provides a rigorous framework for evaluating these digital signals to ensure your brand is perceived as a primary source of truth.
Introduction
Lesson Quiz
Pass at 70%.
1. What is the primary difference between a 'string' and an 'entity' in the context of AI visibility?
2. Which database is most frequently used as a structured data source for LLM training?
3. Why is the 'sameAs' property important in Organization Schema?
4. What should you do if an audit reveals 'entity confusion' with a similarly named competitor?
5. In an entity audit, what does 'relational density' refer to?
6. Which Schema property is best for asserting that a brand is an expert in a specific field?
7. An LLM refuses to recommend a brand. According to the lesson, what is a likely cause?
8. What is a 'Knowledge Graph ID' (kgmid)?
9. How does 'Person' schema contribute to a brand's authority?
10. During an audit, you find the company is mentioned on Reddit in a negative context. Why does this matter for AI visibility?
