Ontograph FAQs

Question 1

How can I access the integrated data from Ontograph?

Accepted Answer

Integrated data is available through several outputs: downloadable CSV tables from periodically run graph queries, pre-built Neo4j database exports for powerful local querying, and a Streamable HTTP MCP endpoint for programmatic access.

Question 2

Who benefits most from using Ontograph?

Accepted Answer

Ontograph is ideal for LLM agents and human researchers who need to perform complex, integrative queries that span multiple biomedical knowledge graphs, enabling deeper insights into biological systems that are difficult to achieve with resource-specific APIs.

Question 3

How does Ontograph integrate data from different sources?

Accepted Answer

It employs HPC pipelines for efficient aggregation, leverages ontologies for semantic alignment, and utilizes LLM embeddings for sophisticated data integration strategies, pulling from major biomedical sources like EMBL-EBI, MONARCH, and ROBOKOP.

Question 4

What is Ontograph?

Accepted Answer

Ontograph is an HPC pipeline that aggregates diverse biomedical knowledge graphs using ontologies and LLM embeddings, designed to facilitate integrated queries across multiple resources that single APIs cannot span.

Question 5

What types of biomedical data does Ontograph integrate?

Accepted Answer

Ontograph integrates a wide array of biomedical resources, including data from the International Mouse Phenotyping Consortium (IMPC), GWAS Catalog, OpenTargets, OLS, Metabolights, ChEMBL, Reactome, and many other gene, phenotype, and pathway databases.

Ontograph

Ontograph

Key Features

Use Cases

Key Features

Use Cases