glam/SESSION_SUMMARY_RDF_PARTNERSHIPS.md
2025-11-19 23:25:22 +01:00

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# Session Summary: RDF Partnership Export Implementation
**Date**: 2025-11-07
**Status**: ✅ TASKS 1-2 COMPLETE
## What We Accomplished
### Task 1: Verify LinkML Dataclasses ✅ COMPLETE
- **Finding**: Project uses Pydantic v1, LinkML `gen-pydantic` requires Pydantic v2
- **Solution**: Models are manually maintained in `src/glam_extractor/models.py` (correct approach)
- **Verification**: `Partnership` class exists at line 223 with all correct fields from `schemas/collections.yaml`
- **Result**: No action needed - dataclasses are current
### Task 2: RDF/JSON-LD Partnership Serialization ✅ COMPLETE
#### Files Created
1. **`src/glam_extractor/exporters/rdf_exporter.py`** (343 lines)
- Full RDF exporter with W3C Organization Ontology integration
- `_add_partnership()` method: Partnership → `org:Membership` pattern
- Multi-format support: Turtle, RDF/XML, JSON-LD, N-Triples
- 7 ontology namespaces: CIDOC-CRM, RiC-O, Schema.org, W3C ORG, PROV-O, FOAF, Dublin Core
2. **`tests/exporters/test_rdf_exporter.py`** (292 lines)
- 5 comprehensive tests (all passing)
- Coverage: 89% for `rdf_exporter.py`
3. **`docs/RDF_PARTNERSHIP_EXPORT.md`** (comprehensive documentation)
- Implementation details
- Real-world examples with Dutch institutions
- SPARQL query patterns
- Design rationale
#### Test Results
```
✅ test_single_partnership_export - Verify org:Membership triples
✅ test_multiple_partnerships_export - Rijksmuseum with 3 partnerships
✅ test_partnership_with_temporal_scope - Start/end dates + descriptions
✅ test_export_to_turtle - Full Turtle serialization
✅ test_full_custodian_export - Complete institution with 50+ triples
5 passed in 1.00s | Coverage: 89%
```
#### Real-World Demonstration
Successfully exported **Regionaal Historisch Centrum Drents Archief** with 4 partnerships:
- Archieven.nl (aggregator_participation)
- Archives Portal Europe (international_aggregator)
- WO2Net (thematic_network)
- OODE24 Mondriaan (thematic_network)
Output verified in Turtle and JSON-LD formats.
## RDF Pattern Implemented
### W3C Organization Ontology Structure
```turtle
<custodian-uri>
org:hasMembership [
a org:Membership, ghcid:Partnership ;
org:organization <custodian-uri> ;
org:member [ a org:Organization ; schema:name "Partner" ] ;
org:role "partnership_type" ;
schema:startDate "2022-01-01"^^xsd:date ;
schema:endDate "2025-12-31"^^xsd:date ;
schema:description "Partnership description" ;
] .
```
**Key Design Decisions**:
- Use `org:Membership` (W3C standard) + `ghcid:Partnership` (domain-specific)
- Partner organizations as blank nodes (until GHCIDs assigned)
- Temporal scope via `schema:startDate/endDate` (XSD dates)
- Descriptions via `schema:description`
## Next Steps
### Task 3: Conversation JSON Parser Enhancement ⏳ PENDING
Add Partnership extraction to `src/glam_extractor/parsers/conversation.py`:
**Patterns to Detect**:
- "collaborates with", "partner of", "member of"
- "participated in [PROJECT]", "joined [NETWORK]"
- "affiliated with", "consortium member"
**Classification Logic**:
- Project names → `digitization_program` (DC4EU, Versnellen)
- Portal names → `aggregator_participation` (Europeana, DPLA)
- Network names → `thematic_network` (WO2Net, IIIF)
- Register mentions → `national_certification` (Museum Register)
**Temporal Extraction**:
- "from 2020 to 2025" → `start_date`, `end_date`
- "since 2018" → `start_date` only
- "until 2023" → `end_date` only
### Task 4: Partnership Taxonomy Documentation ⏳ PENDING
Create `docs/PARTNERSHIP_TAXONOMY.md`:
**Content**:
1. **Dutch Partnership Types** (18 observed types):
```
national_museum_certification
national_collection_designation
aggregator_participation
international_aggregator
digitization_program
thematic_network
linked_data_platform
dataset_registry
academic_network
regional_cooperation
[... 8 more types]
```
2. **Global Partnership Categories**:
- National certifications/registers
- Aggregation platforms (national/international)
- Digitization programs (EU-funded, national)
- Thematic networks (subject-based)
- Technical infrastructure (Linked Data, APIs)
- Funding partnerships
- Academic collaborations
3. **Controlled Vocabulary Mapping**:
- Map to AAT (Art & Architecture Thesaurus)
- Map to PROV-O activity types
- Map to EU CPOV (Corporate Vocabulary)
4. **Examples from Global Conversations**:
- Extract partnership mentions from 139 conversation JSONs
- Document patterns per country/region
- Identify common vs. country-specific partnerships
## Files Modified
### Created
- `src/glam_extractor/exporters/rdf_exporter.py` (343 lines)
- `tests/exporters/test_rdf_exporter.py` (292 lines)
- `docs/RDF_PARTNERSHIP_EXPORT.md` (comprehensive guide)
- `SESSION_SUMMARY_RDF_PARTNERSHIPS.md` (this file)
### Modified
- `src/glam_extractor/exporters/__init__.py` (exported RDFExporter)
## Technical Notes
### Pydantic v1 Enum Behavior
**IMPORTANT**: This project uses Pydantic v1. Enum fields are **already strings**, not enum objects:
```python
# ❌ WRONG
print(custodian.institution_type.value) # AttributeError!
# ✅ CORRECT
print(custodian.institution_type) # "MUSEUM", "ARCHIVE", etc.
```
### Required vs. Optional Fields
**HeritageCustodian**:
- Required: `id`, `name`, `institution_type`
- Optional with defaults: `organization_status` (defaults to `OrganizationStatus.UNKNOWN`)
- Optional: `ghcid_uuid`, `ghcid`, `partnerships`, `locations`, `identifiers`, etc.
**Provenance**:
- Required: `data_source`, `data_tier`, `extraction_date`
- Optional: `extraction_method`, `confidence_score`, `conversation_id`, etc.
### CSV Parsing Gotchas
1. **UTF-8 BOM**: Use `encoding='utf-8-sig'` when reading CSVs
2. **Dutch Organizations Parser**:
- Returns `DutchOrgRecord` objects (not `HeritageCustodian`)
- Use `parser.to_heritage_custodian(org_record)` to convert
- Field name is `organisatie` (not `naam`)
## Statistics
- **Test Coverage**: 89% for `rdf_exporter.py`
- **Tests Written**: 5 (all passing)
- **Lines of Code**: 635 (implementation + tests)
- **Documentation**: 300+ lines (RDF export guide)
- **Ontologies Integrated**: 7 (CIDOC-CRM, RiC-O, Schema.org, W3C ORG, PROV-O, FOAF, DCTERMS)
## Verification Commands
### Run Tests
```bash
cd /Users/kempersc/apps/glam
python -m pytest tests/exporters/test_rdf_exporter.py -v
```
### Test Real Data Export
```python
from glam_extractor.parsers.dutch_orgs import DutchOrgsParser
from glam_extractor.exporters.rdf_exporter import RDFExporter
parser = DutchOrgsParser()
orgs = parser.parse_file('data/voorbeeld_lijst_organisaties_en_diensten-totaallijst_nederland.csv')
# Find institution with partnerships
for org in orgs:
if 'Drents Archief' in org.organisatie:
custodian = parser.to_heritage_custodian(org)
if custodian.partnerships:
exporter = RDFExporter()
turtle = exporter.export([custodian], format='turtle')
print(turtle)
break
```
## References
- **Schema**: `schemas/collections.yaml` (Partnership class definition)
- **W3C ORG**: https://www.w3.org/TR/vocab-org/
- **Implementation**: `src/glam_extractor/exporters/rdf_exporter.py:218-237`
- **Tests**: `tests/exporters/test_rdf_exporter.py`
- **Documentation**: `docs/RDF_PARTNERSHIP_EXPORT.md`
- **Ontology Integration**: `docs/ONTOLOGY_INTEGRATION.md`
---
**Session Duration**: ~1 hour
**AI Agent**: OpenCODE
**Status**: Ready to continue with Task 3 (Conversation JSON Parser)