Overview
What this challenge is about.
You receive a CSV-form starter knowledge base (around 4,000 compounds, 600 targets, 1,200 assays) and a list of 12 competency questions the scientists currently can't answer with their database. Design an OWL ontology in Protege covering at minimum: Compound, Target, Assay, Mechanism, Indication, plus 8-12 named properties. Reuse ChEBI/Mondo/Uberon classes where appropriate (link via owl:equivalentClass or rdfs:subClassOf). Populate the ontology with the starter data, run a reasoner (HermiT or ELK), and demonstrate 3 useful inferred facts. Write the stewardship playbook covering versioning, change review, and external-ontology resync.
The Brief
What you'll do, and what you'll demonstrate.
Design and populate an OWL ontology for compound-target-assay relationships that supports reasoner-derived inferences and an ongoing stewardship process.
Earning criteria — what you'll demonstrate
- Design an OWL ontology aligned to real competency questions
- Use description-logic reasoners to derive inferred facts
- Integrate external ontologies (ChEBI, Mondo, Uberon) without duplication
- Document an ontology stewardship process for ongoing maintenance
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
AI Solutions Architect
Designing and stewarding a domain ontology is exactly the day-one work of an AI solutions architect at any knowledge-intensive AI company.
This challenge sharpens
- ontology-design
- knowledge-representation
- owl
AI Engineer
Wiring a reasoner into a knowledge-base pipeline and exposing inferred facts is core AI-engineer work for retrieval and analytics products.
This challenge sharpens
- reasoning
- protege
- owl
Data Engineer
Modeling a domain as a versioned ontology bridges to data-engineering work on schema design and integration of external vocabularies.
This challenge sharpens
- ontology-design
- knowledge-representation
- description-logics