01
Where it fits and where it does not
Use these four checks before committing implementation time.
- Use it when
- Semantic annotation, knowledge graphs, terminology normalization, and cross-dataset integration.
- Limits
- Coverage and maintenance vary by ontology; overlap, versioning, and term-selection policy still require local stewardship.
- Best for
- Discovery and Laboratory and Clinical and Omics teams working across Plan → Harmonize → Learn + reuse.
- Maturity
- EstablishedSuitable for production assessment. Pin the exact release and any implementation profile.
02
See it in the workflow
This view shows the input, the change the standard introduces, and the resulting output.
- InputWhat starts
Discovery and Laboratory and Clinical and Omics source data, metadata, and local mappings
- OBO FoundryWhat changes
Use OBO Foundry as a pinned ontology ecosystem across Plan → Harmonize → Learn + reuse
- OutputWhat becomes possible
A handoff the next system or team can validate against the same release
03
A concrete example
A sample knowledge graph uses UBERON for anatomy, CL for cell type, and disease ontologies with stable identifiers.
Why it matters: Stable identifiers and logical relationships support semantic features and retrieval, but ontology choice can encode unwanted granularity or bias.
04
What it fits with
Provides domain terms used by ISA, Bioschemas, Allotrope mappings, and bespoke schemas; ontologies may align through shared upper-level patterns.
- Metadata profileExpmeta
Both support Omics and Discovery and Laboratory work and meet around Plan, Harmonize, Learn + reuse. Compare their roles before treating them as interchangeable.
Explore relationship - Data model / schemaISA
Both support Discovery and Laboratory and Omics work and meet around Plan, Harmonize. Compare their roles before treating them as interchangeable.
Explore relationship - Metadata profileSDRF-Proteomics
Both support Omics and Laboratory work and meet around Plan, Harmonize, Learn + reuse. Compare their roles before treating them as interchangeable.
Explore relationship - Metadata vocabularyDPV
Both support Clinical and Omics work and meet around Plan, Harmonize, Learn + reuse. Compare their roles before treating them as interchangeable.
Explore relationship
05
Implementation starter
Start with one bounded handoff. Pin, test, and review it before scaling.
Define one handoff, its accountable owner, and the decision OBO Foundry must support.
Pin the exact version and companion artifacts: Evolving principles + live registry; releases vary.
Map one representative input to the required ontology ecosystem artifacts.
Test the result against the canonical source and record every exception.
Preserve the source data, mappings, and review evidence before scaling.
06
Test the main limitation
Coverage and maintenance vary by ontology; overlap, versioning, and term-selection policy still require local stewardship.
Run one representative end-to-end pilot and record exactly where OBO Foundry loses context, needs an extension, or depends on another standard.
Machine-readable output may still be unfit for analysis or ML.
Test the output for missing context, provenance, terminology alignment, time leakage, and the intended downstream decision. Stable identifiers and logical relationships support semantic features and retrieval, but ontology choice can encode unwanted granularity or bias.
07
Official resources
Specifications, diagrams, examples, and guides from the organizations that maintain them.
OBO Foundry principles
Official publisher or steward guidance for this ontology ecosystem profile.
- Publisher
- OBO Foundry community