6b3b2abe4f
73 stackexchange-grounded, 516 model-knowledge (ESCO-seeded gemma3), 0 review_needed (calibration caveat documented). Adapters rebuilt with the full competence sets; final REPORT numbers; all verify checks pass. Known gap: engineering-manager lacks an ESCO package counterpart. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01PDKeXvpT6tENSvyQGLV1Uq
1.2 KiB
esco_uri, esco_label, relation, onet_soc, source, confidence, qa_count, generator, generated
| esco_uri | esco_label | relation | onet_soc | source | confidence | qa_count | generator | generated |
|---|---|---|---|---|---|---|---|---|
| http://data.europa.eu/esco/skill/5da8018b-ae85-4cde-ad93-0394369018f3 | SPARQL | optional | 15-1242.00 | model-knowledge | medium | 0 | gemma3:27b (prompt-designed and spot-checked by Claude) | 2026-07-10 |
SPARQL — Database Administrator
For a Database Administrator, SPARQL isn't about replacing SQL; it’s about extending your toolkit for increasingly common data integration scenarios. You'll likely encounter SPARQL when dealing with RDF (Resource Description Framework) databases – think knowledge graphs, semantic web data, or metadata repositories. Daily tasks might involve validating the integrity of RDF data loaded into a triple store (a database designed for RDF), troubleshooting complex queries written by data scientists accessing that data, and potentially even writing basic SPARQL queries yourself to investigate data quality issues or perform ad-hoc reporting when SQL isn't suitable. It’s less about full schema management and more about understanding how linked data is structured and queried.
Weitere Anreicherung
Stage-2 source for future practitioner grounding: arXiv cs.SE (software-engineering preprints).