# How do we extract and link relationships from scientific research?
We extract relationships from scientific research by using large language models (LLMs) to scan studies and pull out each of the components reported (i.e., variables, and the following as available: mechanism type, statistic type, statistic value, confidence interval, and p-value). For every extraction, traceability to the original source is preserved by storing all data at the highest resolution, true-to-source, along with links to the underlying sources, which are provided to users.
Once extracted, we link findings together by mapping each of the variables in a relationship to a higher-level concept or topic grounded in external ontologies such as MESH, ICD-10, SNOMED CT, and DSM-5, LNC, and Wikidata. For example, a variable named “daily aspirin usage” would be assigned to the topic of “Aspirin” \[MESH ID: [D001241](https://meshb.nlm.nih.gov/record/ui?ui=D001241), Wikidata ID: [Q18216](https://www.wikidata.org/wiki/Q18216)], and thus discoverable by searching for that associated topic. The result is an up-to-date and interoperable graph of evidence-based findings.
---
# Agent Instructions: Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter:
```
GET https://docs.system.com/system/how-system-works/system-platform/how-do-we-extract-and-link-relationships-from-scientific-research.md?ask=
```
The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.