- Add fix_yaml_history.py and fix_yaml_history_v2.py for cleaning up
malformed ghcid_history entries with duplicate/redundant data
- Update load_custodians_to_ducklake.py for DuckDB lakehouse loading
- Update migrate_web_archives.py for web archive management
- Update deploy.sh with improvements
- Ignore entire data/ducklake/ directory (generated databases)
Enrichment scripts for country-specific city data:
- enrich_austrian_cities.py, enrich_belgian_cities.py, enrich_belgian_v2.py
- enrich_bulgarian_cities.py, enrich_czech_cities.py, enrich_czech_cities_fast.py
- enrich_japanese_cities.py, enrich_swiss_isil_cities.py, enrich_cities_google.py
Location resolution utilities:
- resolve_cities_from_file_coords.py - Resolve cities using coordinates in filenames
- resolve_cities_wikidata.py - Use Wikidata P131 for city resolution
- resolve_country_codes.py - Standardize country codes
- resolve_cz_xx_regions.py - Fix Czech XX region codes
- resolve_locations_by_name.py - Name-based location lookup
- resolve_regions_from_city.py - Derive regions from city data
- update_ghcid_with_geonames.py - Update GHCIDs with GeoNames data
CH-Annotator integration:
- create_custodian_from_ch_annotator.py - Create custodians from annotations
- add_ch_annotator_location_claims.py - Add location claims
- extract_locations_ch_annotator.py - Extract locations from annotations
Migration and fixes:
- migrate_egyptian_from_ch.py - Migrate Egyptian data
- migrate_web_archives.py - Migrate web archive data
- fix_belgian_cities.py - Fix Belgian city data
Remove 229 custodian YAML files containing invalid characters in GHCIDs:
- Ampersand (&) in abbreviations (e.g., BM&HS, UNL&AG, DR&IMSM)
- Parentheses in abbreviations (e.g., WHO(RA, VK(, SL()
- Unicode characters in filenames (Ö, Ä, Å, É, İ, Ż, etc.)
These files are replaced with corrected versions using alphabetic-only
abbreviations per AGENTS.md Rule 8 (Special Characters MUST Be Excluded).
Related scripts updated for location resolution.
- Introduced `llm_extract_archiveslab.py` script for entity and relationship extraction using LLMAnnotator with GLAM-NER v1.7.0.
- Replaced regex-based extraction with generative LLM inference.
- Added functions for loading markdown content, converting annotation sessions to dictionaries, and generating extraction statistics.
- Implemented comprehensive logging of extraction results, including counts of entities, relationships, and specific types like heritage institutions and persons.
- Results and statistics are saved in JSON format for further analysis.
- Implemented `generate_mermaid_with_instances.py` to create ER diagrams that include all classes, relationships, enum values, and instance data.
- Loaded instance data from YAML files and enriched enum definitions with meaningful annotations.
- Configured output paths for generated diagrams in both frontend and schema directories.
- Added support for excluding technical classes and limiting the number of displayed enum and instance values for readability.
- Implemented a Python script to validate KB library YAML files for required fields and data quality.
- Analyzed enrichment coverage from Wikidata and Google Maps, generating statistics.
- Created a comprehensive markdown report summarizing validation results and enrichment quality.
- Included error handling for file loading and validation processes.
- Generated JSON statistics for further analysis.
- Introduced a comprehensive class diagram for the heritage custodian observation reconstruction schema.
- Defined multiple classes including AllocationAgency, ArchiveOrganizationType, AuxiliaryDigitalPlatform, and others, with relevant attributes and relationships.
- Established inheritance and associations among classes to represent complex relationships within the schema.
- Generated on 2025-11-28, version 0.9.0, excluding the Container class.
- Implemented a Python script that fetches and enriches entries from the NDE Register using data from Wikidata.
- Utilized the Wikibase REST API and SPARQL endpoints for data retrieval.
- Added logging for tracking progress and errors during the enrichment process.
- Configured rate limiting based on authentication status for API requests.
- Created a structured output in YAML format, including detailed enrichment data.
- Generated a log file summarizing the enrichment process and results.
- Created PlantUML diagrams for custodian types, full schema, legal status, and organizational structure.
- Implemented a script to generate GraphViz DOT diagrams from OWL/RDF ontology files.
- Developed a script to generate UML diagrams from modular LinkML schema, supporting both Mermaid and PlantUML formats.
- Enhanced class definitions and relationships in UML diagrams to reflect the latest schema updates.
- Created the Country class with ISO 3166-1 alpha-2 and alpha-3 codes, ensuring minimal design without additional metadata.
- Integrated the Country class into CustodianPlace and LegalForm schemas to support country-specific feature types and legal forms.
- Removed duplicate keys in FeatureTypeEnum.yaml, resulting in 294 unique feature types.
- Eliminated "Hypernyms:" text from FeatureTypeEnum descriptions, verifying that semantic relationships are now conveyed through ontology mappings.
- Created example instance file demonstrating integration of Country with CustodianPlace and LegalForm.
- Updated documentation to reflect the completion of the Country class implementation and hypernyms removal.
- Created SHACL shapes for validating temporal consistency and bidirectional relationships in custodial collections and staff observations.
- Implemented a Python script to validate RDF data against the defined SHACL shapes using the pyshacl library.
- Added command-line interface for validation with options for specifying data formats and output reports.
- Included detailed error handling and reporting for validation results.
- Implemented `owl_to_mermaid.py` to convert OWL/Turtle files into Mermaid class diagrams.
- Implemented `owl_to_plantuml.py` to convert OWL/Turtle files into PlantUML class diagrams.
- Added two new PlantUML files for custodian multi-aspect diagrams.
- Introduced custodian_hub_v3.mmd, custodian_hub_v4_final.mmd, and custodian_hub_v5_FINAL.mmd for Mermaid representation.
- Created custodian_hub_FINAL.puml and custodian_hub_v3.puml for PlantUML representation.
- Defined entities such as CustodianReconstruction, Identifier, TimeSpan, Agent, CustodianName, CustodianObservation, ReconstructionActivity, Appellation, ConfidenceMeasure, Custodian, LanguageCode, and SourceDocument.
- Established relationships and associations between entities, including temporal extents, observations, and reconstruction activities.
- Incorporated enumerations for various types, statuses, and classifications relevant to custodians and their activities.
- 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.
