Knowledge Graph Optimisation
Some AI systems check a structured database before they even bother crawling the open web. If your brand isn't in it, or the entry is thin and out of date, that's the gap this work closes.
What a knowledge graph entry actually does for you
A knowledge graph is a structured database of facts about entities, people, places, organisations, products, connected to each other in defined relationships rather than sitting in free-flowing prose. Wikidata is the largest open one. Google's Knowledge Graph is a separate, proprietary one that powers knowledge panels and feeds a lot of downstream AI retrieval.
Being present in one of these graphs, with accurate, current facts, gives a model something firmer to work from than an inference pieced together from scattered web pages. It's a different kind of signal to a well-written page, more structured, more machine-native, and it tends to carry real weight when it exists.
It's worth being upfront about the limits here. Wikidata has fairly open inclusion standards. Wikipedia, which is a separate project with its own much stricter notability requirements, is a different and harder bar to clear, and not every brand should expect to meet it. We'll tell you honestly which is realistic for your situation rather than promising both.
What happens when the graph entry is missing or thin
Without a knowledge graph entry, a model has to infer basic facts about your brand from whatever web content it can find, and it's genuinely inferring, not looking up a confirmed record. That's where you start seeing odd mistakes: a founding year that's slightly off, a headquarters location that hasn't been true in years, a category classification that's close but not quite right.
A properly maintained entry doesn't guarantee a model will always use it correctly, but it gives it a solid, structured fact to reach for instead of stitching one together from secondhand mentions.
How we approach knowledge graph work
Eligibility check
We assess honestly whether Wikidata, Wikipedia, or both are realistic for your brand, since the two have very different bars for inclusion.
Structured data submission
Where eligible, we prepare and submit properly sourced, structured entries rather than thin, unsupported ones likely to be reverted.
Cross-linking
sameAs links tie your knowledge graph entries back to your website and verified profiles, reinforcing that they all describe the same entity.
Monitoring for drift
Open knowledge graphs can be edited by anyone. We check periodically for vandalism, outdated facts, or accidental errors and correct them.
Expansion where relevant
For some categories, industry-specific structured directories matter as much as the major public graphs, and we extend the work there when it's worthwhile.
What you actually get
- An honest eligibility assessment for Wikidata and Wikipedia
- Properly sourced structured entries where eligible
- sameAs links connecting your graph entries to your verified profiles
- Periodic monitoring for vandalism, drift or factual errors
- Recommendations for relevant industry-specific structured directories
Works well alongside
- Entity SEO, for the site-level identity work a graph entry depends on.
- AI Citation Building, to earn the third-party sources that support a graph entry's notability.
Knowledge graph questions worth a straight answer
What's the actual difference between Wikidata and Wikipedia?+
Can you guarantee my brand gets a Wikipedia page?+
What if someone edits or vandalises our entry?+
How long does it take to get a new entry live?+
Want an honest read on your knowledge graph presence?
A free audit shows exactly how ChatGPT, Gemini and Perplexity answer your category today, and where Knowledge Graph Optimisation could close the gap.
or call 1300 138 708