e5a532a8bc
- 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.
87 lines
2.4 KiB
Python
87 lines
2.4 KiB
Python
#!/usr/bin/env python3
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"""
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Direct Wikidata query to inspect Q28149782 (Diocesan Library).
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"""
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import requests
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import json
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SPARQL_ENDPOINT = "https://query.wikidata.org/sparql"
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USER_AGENT = "GLAM-Tunisia-Debug/1.0"
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# Query to get all properties of Q28149782
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query = """
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SELECT ?property ?propertyLabel ?value ?valueLabel WHERE {
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wd:Q28149782 ?p ?value .
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?property wikibase:directClaim ?p .
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SERVICE wikibase:label { bd:serviceParam wikibase:language "en,fr,ar" . }
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}
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LIMIT 100
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"""
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headers = {'User-Agent': USER_AGENT}
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params = {
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'query': query,
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'format': 'json'
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}
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print("Querying Wikidata for Q28149782 (Diocesan Library of Tunis)")
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print("=" * 60)
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response = requests.get(SPARQL_ENDPOINT, params=params, headers=headers, timeout=30)
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response.raise_for_status()
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results = response.json()
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bindings = results.get("results", {}).get("bindings", [])
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print(f"Found {len(bindings)} properties\n")
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# Group by property
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properties = {}
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for binding in bindings:
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prop = binding.get("propertyLabel", {}).get("value", "unknown")
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val = binding.get("valueLabel", {}).get("value", binding.get("value", {}).get("value", ""))
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if prop not in properties:
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properties[prop] = []
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properties[prop].append(val)
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# Print relevant properties
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important_props = ['instance of', 'country', 'located in the administrative territorial entity',
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'coordinate location', 'label']
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for prop in important_props:
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if prop in properties:
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print(f"{prop}:")
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for val in properties[prop]:
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print(f" - {val}")
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print("\n" + "=" * 60)
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# Now get the specific instance type QID
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query2 = """
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SELECT ?type ?typeLabel WHERE {
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wd:Q28149782 wdt:P31 ?type .
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SERVICE wikibase:label { bd:serviceParam wikibase:language "en,fr" . }
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}
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"""
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params2 = {
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'query': query2,
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'format': 'json'
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}
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print("\nInstance types (P31) for Q28149782:")
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response2 = requests.get(SPARQL_ENDPOINT, params=params2, headers=headers, timeout=30)
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response2.raise_for_status()
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results2 = response2.json()
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bindings2 = results2.get("results", {}).get("bindings", [])
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for binding in bindings2:
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type_uri = binding.get("type", {}).get("value", "")
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type_qid = type_uri.split("/")[-1] if type_uri else "unknown"
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type_label = binding.get("typeLabel", {}).get("value", "")
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print(f" {type_qid}: {type_label}")
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print("\nCheck if Q105338594 is in our LIBRARY type mapping...")
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