# 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)