Description-Logic Reasoner for Insurance-Policy Coverage Checks
Overview
What this challenge is about.
You receive 50 representative coverage rules in plain English (from the current rule engine) and a sample of 1,000 anonymized claim cases with the current engine's outcomes (covered/not-covered with reason codes). Model the rules in OWL using description-logic constructs (class restrictions, property chains, disjointness) and run a reasoner (HermiT) to classify each claim. Build a justification-trace exporter that, for a given claim, shows the inference chain. Compare your reasoner's outcomes to the current engine on the 1,000 claims and characterize disagreements. Write a 2-page memo to claims operations.
The Brief
What you'll do, and what you'll demonstrate.
Replace an if/else coverage engine with a description-logic ontology that produces auditable, explainable coverage decisions.
Earning criteria — what you'll demonstrate
- Model real business rules as description-logic axioms
- Use a reasoner to derive coverage outcomes
- Extract and format reasoner justification traces
- Compare a logic-based system with a procedural baseline
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
Replacing procedural rule engines with description-logic ontologies is exactly the kind of architecture work AI solutions architects ship at insurance and regulated-services firms.
This challenge sharpens
- description-logics
- owl
- knowledge-representation
AI Engineer
Wiring a reasoner into a production pipeline and exposing justification traces is core AI-engineer work in regulated industries.
This challenge sharpens
- reasoning
- rule-engines
- explainability
Data Engineer
Designing ontology-backed rule systems with versioned classification harnesses transfers to data-engineering work on knowledge-pipeline platforms.
This challenge sharpens
- owl
- knowledge-representation
- rule-engines