- Implemented a new script to extract full metadata from 149 archive detail pages on archive-in-thueringen.de.
- Extracted data includes addresses, emails, phones, directors, collection sizes, opening hours, histories, and more.
- Introduced structured data parsing and error handling for robust data extraction.
- Added rate limiting to respect server load and improve scraping efficiency.
- Results are saved in a JSON format with detailed metadata about the extraction process.
- Introduced `test_nlp_extractor.py` with unit tests for the InstitutionExtractor, covering various extraction patterns (ISIL, Wikidata, VIAF, city names) and ensuring proper classification of institutions (museum, library, archive).
- Added tests for extracted entities and result handling to validate the extraction process.
- Created `test_partnership_rdf_integration.py` to validate the end-to-end process of extracting partnerships from a conversation and exporting them to RDF format.
- Implemented tests for temporal properties in partnerships and ensured compliance with W3C Organization Ontology patterns.
- Verified that extracted partnerships are correctly linked with PROV-O provenance metadata.
- Add Wikidata Q-numbers to 8 Brazilian institutions
- Coverage: 56/212 institutions (26.4%, +5.6pp gain)
- All Q-numbers validated via Wikidata authenticated API
- Largest single batch gain yet
- Note: Duplicate entries detected, deduplication needed
Q-numbers added:
- Q10333651 - Museu da Borracha
- Q10387829 - UFAC Repository
- Q10345196 - Parque Memorial Quilombo dos Palmares
- Q1434444 - Teatro Amazonas
- Q116921020 - Centro Cultural dos Povos da Amazônia
- Q7894381 - UNIFAP
- Q16496091 - Arquivo Público do Estado da Bahia
- Q56695457 - Museu de Arqueologia e Etnologia da UFPR
