Google AI Overviews mentions our roofing fasteners and structural connectors, but we don't appear in Wikidata or building material knowledge bases. How do we get listed there?

Knowledge bases like Wikidata, DBpedia, and industry-specific taxonomies feed AI engines their reference layer. If your brand isn't represented there, AI systems have less authoritative context to draw from—they may cite competitors or generic product descriptions instead. Start by auditing whether a Wikidata entry exists for your company at all. Search wikidata.org directly or use a tool that queries the SPARQL endpoint. If nothing exists, you'll need to create one. This requires a Wikidata account and basic familiarity with their property system, but it's free and the community reviews submissions within days.

Second, map your product categories to Schema.org types that align with how building materials are classified. Use Organization + Brand schema on your homepage, and Product schema on category pages, ensuring you reference industry standards (like ASTM classifications for fasteners). This signals to AI crawlers what you manufacture and how it relates to established taxonomies.

Third, contact industry databases and trade associations relevant to building materials—many maintain proprietary or semi-open knowledge graphs. Request inclusion in their supplier directories; these often feed into broader AI training datasets.

To verify progress, rerun your Google AI Overviews searches in 2–3 weeks and check whether Wikidata now shows up in citation trails. Use your Google Search Console to confirm Google's crawler is indexing your schema markup. The lag between submission and AI citation can be 4–8 weeks, so this is a patient play.