Geological features are named entities. “Mount Everest” is an entity. “The San Andreas Fault” is an entity. “The Pierre Shale Formation” is an entity.

For researchers in the geospatial domain, linking your content to these distinct entities is the bedrock of MCP-SEO.

Disambiguation via Wikidata

“Paris” is a city in France. “Paris” is also a city in Texas. “Paris” is also a rock formation (hypothetically). To ensure an AI understands you are talking about the rock formation, you must link to its Wikidata ID (e.g., Q12345).

"about": {
  "@type": "Place",
  "name": "Pierre Shale",
  "sameAs": "https://www.wikidata.org/wiki/Q7192237"
}

Grounding Your Research

When you publish a paper on “Soil Stability in South Dakota,” and you explicitly link the entities to the Knowledge Graph:

  1. Search Engines understand the context immediately.
  2. AI Agents can traverse the graph to find related entities (“What other formations are in the Cretaceous period?”).

You are not just posting a PDF; you are adding a node to the global scientific graph. This prevents the LLM from conflating similarly named formations in different continents, ensuring your research is cited correctly in global meta-analyses.

The “Stratigraphy” of Data

Just as geologists use stratigraphy to date rock layers, AI models use “Temporal Layers” to date information. However, training data is often flattened. A fact from 2010 sits next to a fact from 2025.

The Geological Method for Content: Explicitly date your assertions. “In the Late 2024 Era, the standard was X.” “In the Current 2026 Era, the standard is Y.” By defining the effective era of your facts, you help the model construct a chronological timeline of truth, preventing it from serving “fossilized knowledge” as fresh insight.

Glossary of Terms

  • Agentic Web: The specialized layer of the internet optimized for autonomous agents rather than human browsers.
  • RAG (Retrieval-Augmented Generation): The process where an LLM retrieves external data to ground its response.
  • Vector Database: A database that stores data as high-dimensional vectors, enabling semantic search.
  • Grounding: The act of connecting an AI’s generation to a verifiable source of truth to prevent hallucination.
  • Zero-Shot: The ability of a model to perform a task without seeing any examples.
  • Token: The basic unit of text for an LLM (roughly 0.75 words).
  • Inference Cost: The computational expense required to generate a response.