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import collections
from rdflib import Graph, Literal, URIRef
from rdflib.namespace import RDF, VOID
def generateVoID( # noqa: N802
g, dataset=None, res=None, distinctForPartitions=True # noqa: N803
):
"""
Returns a new graph with a VoID description of the passed dataset
For more info on Vocabulary of Interlinked Datasets (VoID), see:
http://vocab.deri.ie/void
This only makes two passes through the triples (once to detect the types
of things)
The tradeoff is that lots of temporary structures are built up in memory
meaning lots of memory may be consumed :)
I imagine at least a few copies of your original graph.
the distinctForPartitions parameter controls whether
distinctSubjects/objects are tracked for each class/propertyPartition
this requires more memory again
"""
typeMap = collections.defaultdict(set) # noqa: N806
classes = collections.defaultdict(set) # noqa: N806
for e, c in g.subject_objects(RDF.type):
classes[c].add(e)
typeMap[e].add(c)
triples = 0
subjects = set()
objects = set()
properties = set()
classCount = collections.defaultdict(int) # noqa: N806
propCount = collections.defaultdict(int) # noqa: N806
classProps = collections.defaultdict(set) # noqa: N806
classObjects = collections.defaultdict(set) # noqa: N806
propSubjects = collections.defaultdict(set) # noqa: N806
propObjects = collections.defaultdict(set) # noqa: N806
for s, p, o in g:
triples += 1
subjects.add(s)
properties.add(p)
objects.add(o)
# class partitions
if s in typeMap:
for c in typeMap[s]:
classCount[c] += 1
if distinctForPartitions:
classObjects[c].add(o)
classProps[c].add(p)
# property partitions
propCount[p] += 1
if distinctForPartitions:
propObjects[p].add(o)
propSubjects[p].add(s)
if not dataset:
dataset = URIRef("http://example.org/Dataset")
if not res:
res = Graph()
res.add((dataset, RDF.type, VOID.Dataset))
# basic stats
res.add((dataset, VOID.triples, Literal(triples)))
res.add((dataset, VOID.classes, Literal(len(classes))))
res.add((dataset, VOID.distinctObjects, Literal(len(objects))))
res.add((dataset, VOID.distinctSubjects, Literal(len(subjects))))
res.add((dataset, VOID.properties, Literal(len(properties))))
for i, c in enumerate(classes):
part = URIRef(dataset + "_class%d" % i)
res.add((dataset, VOID.classPartition, part))
res.add((part, RDF.type, VOID.Dataset))
res.add((part, VOID.triples, Literal(classCount[c])))
res.add((part, VOID.classes, Literal(1)))
res.add((part, VOID["class"], c))
res.add((part, VOID.entities, Literal(len(classes[c]))))
res.add((part, VOID.distinctSubjects, Literal(len(classes[c]))))
if distinctForPartitions:
res.add((part, VOID.properties, Literal(len(classProps[c]))))
res.add((part, VOID.distinctObjects, Literal(len(classObjects[c]))))
for i, p in enumerate(properties):
part = URIRef(dataset + "_property%d" % i)
res.add((dataset, VOID.propertyPartition, part))
res.add((part, RDF.type, VOID.Dataset))
res.add((part, VOID.triples, Literal(propCount[p])))
res.add((part, VOID.properties, Literal(1)))
res.add((part, VOID.property, p))
if distinctForPartitions:
entities = 0
propClasses = set() # noqa: N806
for s in propSubjects[p]:
if s in typeMap:
entities += 1
for c in typeMap[s]:
propClasses.add(c)
res.add((part, VOID.entities, Literal(entities)))
res.add((part, VOID.classes, Literal(len(propClasses))))
res.add((part, VOID.distinctSubjects, Literal(len(propSubjects[p]))))
res.add((part, VOID.distinctObjects, Literal(len(propObjects[p]))))
return res, dataset
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