The Local Graph

Can local entities and their relationships create better context for people and AI?

Explicit relationships between businesses, places, services, people and events carry context that a standalone page never states, and that context improves both browsing and retrieval.

Hypothesis — not a conclusion

Local questions are relational — what is near, what is related, what is comparable. Flat listings answer none of those directly.

1. Entity
   ↓
2. Relationship
   ↓
3. Local Graph
   ↓
4. Context
   ↓
5. Retrieval

• TheLocalGraph.com ↗

• —Which relationship types matter most for local retrieval
• —Whether graph answers beat flat listing answers at city scale
• —How much of the graph can be inferred versus declared

A listing says what a business is. It rarely says how it relates to its city, its category, the events around it or the people behind it.

Testing has not yet produced documented results.

• Relationship data adds little measurable retrieval value
• Graph maintenance cost grows faster than the benefit
• Existing sources already encode the same context implicitly

• How is an inferred relationship distinguished from a declared one?
• What is the right unit of response: an entity, a list or a graph fragment?

Model a single city as a connected graph and compare retrieval against flat listing data.