本文整理汇总了Python中rdflib.graph.ConjunctiveGraph.predicates方法的典型用法代码示例。如果您正苦于以下问题:Python ConjunctiveGraph.predicates方法的具体用法?Python ConjunctiveGraph.predicates怎么用?Python ConjunctiveGraph.predicates使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类rdflib.graph.ConjunctiveGraph
的用法示例。
在下文中一共展示了ConjunctiveGraph.predicates方法的3个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: get_properties_from_input
# 需要导入模块: from rdflib.graph import ConjunctiveGraph [as 别名]
# 或者: from rdflib.graph.ConjunctiveGraph import predicates [as 别名]
def get_properties_from_input(file, input_format):
input_graph = ConjunctiveGraph()
input_graph.parse(file, format=input_format)
# collapse to single list
property_set = set()
for row in input_graph.predicates():
property_set.add(row)
return property_set
示例2: make_property_graph
# 需要导入模块: from rdflib.graph import ConjunctiveGraph [as 别名]
# 或者: from rdflib.graph.ConjunctiveGraph import predicates [as 别名]
def make_property_graph(properties, args):
graph = ConjunctiveGraph()
output_graph = ConjunctiveGraph()
ontologies = [
'https://raw.githubusercontent.com/monarch-initiative/SEPIO-ontology/master/src/ontology/sepio.owl',
'https://raw.githubusercontent.com/monarch-initiative/GENO-ontology/develop/src/ontology/geno.owl',
'http://purl.obolibrary.org/obo/ro.owl',
'http://purl.obolibrary.org/obo/iao.owl',
'http://purl.obolibrary.org/obo/ero.owl',
'https://raw.githubusercontent.com/jamesmalone/OBAN/master/ontology/oban_core.ttl',
'http://purl.obolibrary.org/obo/pco.owl',
'http://purl.obolibrary.org/obo/xco.owl'
]
for ontology in ontologies:
print("parsing: " + ontology)
try:
graph.parse(ontology, format=rdflib_util.guess_format(ontology))
except SAXParseException as e:
logger.error(e)
logger.error('Retrying: ' + ontology)
graph.parse(ontology, format="turtle")
except OSError as e: # URLError:
# simple retry
logger.error(e)
logger.error('Retrying: ' + ontology)
graph.parse(ontology, format=rdflib_util.guess_format(ontology))
# Get object properties
output_graph = add_property_to_graph(
graph.subjects(RDF['type'], OWL['ObjectProperty']),
output_graph, OWL['ObjectProperty'], properties)
# Get annotation properties
output_graph = add_property_to_graph(
graph.subjects(RDF['type'], OWL['AnnotationProperty']),
output_graph, OWL['AnnotationProperty'], properties)
# Get data properties
output_graph = add_property_to_graph(
graph.subjects(RDF['type'], OWL['DatatypeProperty']),
output_graph, OWL['DatatypeProperty'], properties)
# Hardcoded properties
output_graph.add(
(URIRef('https://monarchinitiative.org/MONARCH_cliqueLeader'),
RDF['type'], OWL['AnnotationProperty']))
output_graph.add(
(URIRef('https://monarchinitiative.org/MONARCH_anonymous'),
RDF['type'], OWL['AnnotationProperty']))
# Check monarch data triple
data_url = "https://data.monarchinitiative.org/ttl/{0}".format(
re.sub(r".*/", "", args.input))
new_url = "https://data.monarchinitiative.org/ttl/{0}".format(
re.sub(r".*/", "", args.output))
if (URIRef(data_url), RDF.type, OWL['Ontology']) in output_graph:
output_graph.remove(URIRef(data_url), RDF.type, OWL['Ontology'])
output_graph.add((URIRef(new_url), RDF.type, OWL['Ontology']))
for row in output_graph.predicates(
DC['source'], OWL['AnnotationProperty']):
if row == RDF['type']:
output_graph.remove(
(DC['source'], RDF['type'], OWL['AnnotationProperty']))
output_graph.add((DC['source'], RDF['type'], OWL['ObjectProperty']))
return output_graph
示例3: pprint
# 需要导入模块: from rdflib.graph import ConjunctiveGraph [as 别名]
# 或者: from rdflib.graph.ConjunctiveGraph import predicates [as 别名]
# Now, with just that, lets see how the system
# recorded *way* too many details about what
# you just asserted as fact.
#
from pprint import pprint
pprint(list(primer))
# just think .whatever((s, p, o))
# here we report on what we know
pprint(list(primer.subjects()))
pprint(list(primer.predicates()))
pprint(list(primer.objects()))
# and other things that make sense
# what do we know about pat?
pprint(list(primer.predicate_objects(myNS.pat)))
# who is what age?
pprint(list(primer.subject_objects(myNS.age)))
# Okay, so lets now work with a bigger
# dataset from the example, and start
# with a fresh new graph.