Building a robust knowledge graph is key for AI visibility, but it's easy to make a few missteps along the way. Here are some common mistakes and how to steer clear of them: Not clearly defining relationships: This is a big one! Don't just list entities; explicitly map how they connect to each other.
For example, show that a 'product' is 'manufactured by' a 'company.' Omitting hierarchies: AI needs to understand the structure of your business. Make sure you're including parent-child relationships where they exist, like categories and subcategories, or departments within a company.
Mixing descriptive fields with measures: Keep your data types clean. Don't put a numerical value into a descriptive text field, or vice-versa. Proper typing helps AI interpret the data correctly.
Failing to assign ownership for validation and correction: Knowledge graphs aren't static. Someone needs to be responsible for ongoing accuracy checks and updates. Otherwise, your graph quickly becomes outdated.
Forgetting security considerations: Especially when combining sensitive data, ignoring who has access and how the data is protected is a huge oversight. When your graph gets complex, especially if it combines several systems or supports critical AI use cases, professional help ensures it’s built right to scale and maintain integrity.