ontology-reuse

name

ontology-reuse

description

Search ontology registries for existing terms to reuse before defining new ones. Use during Step 2 (Knowledge Exploration) and Step 3 (Knowledge Organization), or whenever the user asks to find existing ontology terms, search OLS, look up IRIs, check if a concept already exists, or reuse external terms—e.g. "find an existing class for fatigue", "search OLS for cancer", "is there an ontology term for this?", "reuse terms from OBO Foundry".

Ontology Reuse

Use this skill to search for and evaluate existing ontology terms before creating new ones. Reuse-over-reinvention is a core principle: always search at least one registry before defining a new class or property.

When to Use

• Step 2 (Knowledge Exploration): After extracting concepts from sources, search for each candidate concept in existing ontologies.
• Step 3 (Knowledge Organization): When mapping extracted concepts to existing terminology and flagging gaps.
• Ad-hoc lookups: When the user asks "is there an existing term for X?", "search OLS for Y", or "find an IRI for Z".
• Import-over-reinvention: When multiple candidate terms from the same ontology suggest importing that ontology rather than recreating classes locally.

Workflow

1. Collect candidate concepts

From the extraction table (Step 2) or user request, identify concepts that need a registry lookup.

2. Search OLS

Use the ols_search tool for each candidate concept:

call_tool(name: "ols_search", data: { "query": "cancer related fatigue" })

• Spaces are URL-encoded automatically.
• Use page to paginate (defaults to "0").
• Results are filtered to defining ontologies (isDefiningOntology=true) and include facets by ontologyId and type.

3. Interpret results

The API returns JSON with this structure:

Field	Description
page, totalPages, totalElements	Pagination info
elements[]	Array of matching entities
facetFieldsToCounts.ontologyId	Hit counts per ontology (use to identify which ontologies are most relevant)
facetFieldsToCounts.type	Hit counts by entity type (class, individual, property, etc.)

Each element in elements[] contains:

Field	Description
iri	Full IRI of the entity
curie	Compact URI (e.g. SNOMED:716749005)
label	Array of labels (preferred + alternative)
ontologyId	Source ontology identifier
ontologyPreferredPrefix	Preferred prefix for the source ontology
type	Entity types (e.g. ["class", "entity"])
isObsolete	Whether the term is deprecated
directParent	Direct parent IRIs
directAncestor	All ancestor IRIs
linkedEntities	Map of related entities with their labels and types
shortForm	Short form identifier (e.g. SNOMED_716749005)
numDescendants	Number of descendant terms

4. Evaluate candidates

For each matching term, assess:

1. Semantic fit — Does the term's label and definition match the intended concept?
2. Ontology provenance — Prefer terms from well-maintained, widely-used ontologies (OBO Foundry, SNOMED, NCIt, DOID, etc.).
3. Obsolescence — Skip terms where isObsolete: true.
4. Hierarchy fit — Check directParent and directAncestor to see if the term's position in its source hierarchy aligns with the target ontology's structure.
5. Type match — Confirm the entity type matches what's needed (class vs. individual vs. property).

5. Report findings

Present results to the user as a table:

Concept	Existing Term	Source	IRI	Fit	Action
Cancer-related fatigue	Cancer-related fatigue	SNOMED	SNOMED:716749005	Exact	Reuse
Fatigue	Fatigue	SNOMED	SNOMED:84229001	Broader	Reuse as parent
Custom concept	—	—	—	No match	Define new

Action values:

• Reuse — use the existing IRI directly
• Reuse as parent — use as a superclass for a more specific new term
• Import — import the source ontology (when 3+ terms from the same ontology are reused)
• Define new — no suitable existing term found

6. Apply reuse decisions

When reusing a term in the ontology (during Step 6 — Formalization):

• Add the source ontology's prefix via add_prefix
• Reference the term by its full IRI or CURIE in axioms
• If importing the whole ontology, add an Import(...) axiom with the canonical IRI
• Add AnnotationAssertion(rdfs:seeAlso ...) or provenance annotations linking to the source

Pagination

When results span multiple pages, use the page parameter:

call_tool(name: "ols_search", data: { "query": "fatigue", "page": "1" })

Check totalPages in the response to know how many pages are available. The facetFieldsToCounts.ontologyId facet (returned on every page) shows the total hit distribution across ontologies — use it to decide whether to filter by a specific ontology in a follow-up search.

Complementary Registries

OLS covers OBO Foundry, EFO, SNOMED, and many biomedical ontologies. For broader coverage, also consider manual searches on:

• OBO Foundry — http://obofoundry.org
• BioPortal — https://bioportal.bioontology.org
• OntoBee — http://ontobee.org
• LOV (Linked Open Vocabularies) — https://lov.linkeddata.es

Tool Requirement

This skill requires: ols_search

After learning this skill, call setup_tools(skills: ["ontology-reuse"]) to activate the tool, then use call_tool(name: "ols_search", data: {"query": "...", "page": "0"}) to invoke it.