Overview
What this challenge is about.
Build an NER and entity-linking pipeline for 12,000 climate policy PDFs, evaluate against a benchmark, and publish Linked Data. Get a verifiable certificate.
The scenario
The non-profit (~30 staff) is funded by foundations whose grant criteria explicitly reward Linked Open Data publishing as a measure of research transparency.
The Brief
What you'll do, and what you'll demonstrate.
Link a 12,000-document climate-policy corpus to Wikidata and EuroVoc with measured precision and recall, and publish it as Linked Open Data.
Earning criteria — what you'll demonstrate
- Build an end-to-end entity-linking pipeline against Wikidata and EuroVoc
- Apply Linked Data publishing principles (URIs, sameAs, attribution)
- Evaluate entity-linking quality at precision/recall level
- Author methodology notes appropriate to grant-reporting expectations
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Knowledge Graphs and Semantic Web
Master · Data Engineering
Strong alignment
This challenge maps to Knowledge Graphs and Semantic Web at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Entity Linking
Apply entity linking to solve real industry problems and demonstrate production-level capability.
- Linked Open Data
Apply linked open data to solve real industry problems and demonstrate production-level capability.
- Wikidata
Apply wikidata to solve real industry problems and demonstrate production-level capability.
- Rdf
Apply rdf to solve real industry problems and demonstrate production-level capability.
- Ner
Apply ner to solve real industry problems and demonstrate production-level capability.
- Sparql
Apply sparql to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Data Engineer
Publishing a corpus as Linked Open Data with measured linking quality is the day-to-day of data engineers at research and open-data orgs.
This challenge sharpens
- linked-open-data
- rdf
- entity-linking
NLP Engineer
NER plus disambiguation against a real KG is core NLP-engineer work in any entity-linking product.
This challenge sharpens
- ner
- entity-linking
- wikidata
AI Solutions Architect
Specifying the linked-data architecture and the publishing pipeline is the AI solutions architect's role in open-data engagements.
This challenge sharpens
- linked-open-data
- rdf
- sparql