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import collections
import rdflib
from rdflib import RDF
"""
RDF- and RDFlib-centric Graph utilities.
"""
def graph_to_dot(graph, dot):
"""
Turns graph into dot (graphviz graph drawing format) using pydot.
"""
import pydot
nodes = {}
for s, o in graph.subject_objects():
for i in s, o:
if i not in nodes.keys():
nodes[i] = i
for s, p, o in graph.triples((None, None, None)):
dot.add_edge(pydot.Edge(nodes[s], nodes[o], label=p))
def find_roots(graph, prop, roots=None):
"""
Find the roots in some sort of transitive hierarchy.
find_roots(graph, rdflib.RDFS.subClassOf)
will return a set of all roots of the sub-class hierarchy
Assumes triple of the form (child, prop, parent), i.e. the direction of
RDFS.subClassOf or SKOS.broader
"""
non_roots = set()
if roots is None:
roots = set()
for x, y in graph.subject_objects(prop):
non_roots.add(x)
if x in roots:
roots.remove(x)
if y not in non_roots:
roots.add(y)
return roots
def get_tree(graph,
root,
prop,
mapper=lambda x: x,
sortkey=None,
done=None,
dir='down'):
"""
Return a nested list/tuple structure representing the tree
built by the transitive property given, starting from the root given
i.e.
get_tree(graph,
rdflib.URIRef("http://xmlns.com/foaf/0.1/Person"),
rdflib.RDFS.subClassOf)
will return the structure for the subClassTree below person.
dir='down' assumes triple of the form (child, prop, parent),
i.e. the direction of RDFS.subClassOf or SKOS.broader
Any other dir traverses in the other direction
"""
if done is None:
done = set()
if root in done:
return
done.add(root)
tree = []
if dir == 'down':
branches = graph.subjects(prop, root)
else:
branches = graph.objects(root, prop)
for branch in branches:
t = get_tree(graph, branch, prop, mapper, sortkey, done, dir)
if t:
tree.append(t)
return (mapper(root), sorted(tree, key=sortkey))
VOID = rdflib.Namespace("http://rdfs.org/ns/void#")
DCTERMS = rdflib.Namespace("http://purl.org/dc/terms/")
FOAF = rdflib.Namespace("http://xmlns.com/foaf/0.1/")
def generateVoID(g, dataset=None, res=None, distinctForPartitions=True):
"""
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)
classes = collections.defaultdict(set)
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)
propCount = collections.defaultdict(int)
classProps = collections.defaultdict(set)
classObjects = collections.defaultdict(set)
propSubjects = collections.defaultdict(set)
propObjects = collections.defaultdict(set)
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 = rdflib.URIRef("http://example.org/Dataset")
if not res:
res = rdflib.Graph()
res.add((dataset, RDF.type, VOID.Dataset))
# basic stats
res.add((dataset, VOID.triples, rdflib.Literal(triples)))
res.add((dataset, VOID.classes, rdflib.Literal(len(classes))))
res.add((dataset, VOID.distinctObjects, rdflib.Literal(len(objects))))
res.add((dataset, VOID.distinctSubjects, rdflib.Literal(len(subjects))))
res.add((dataset, VOID.properties, rdflib.Literal(len(properties))))
for i, c in enumerate(classes):
part = rdflib.URIRef(dataset + "_class%d" % i)
res.add((dataset, VOID.classPartition, part))
res.add((part, RDF.type, VOID.Dataset))
res.add((part, VOID.triples, rdflib.Literal(classCount[c])))
res.add((part, VOID.classes, rdflib.Literal(1)))
res.add((part, VOID["class"], c))
res.add((part, VOID.entities, rdflib.Literal(len(classes[c]))))
res.add((part, VOID.distinctSubjects, rdflib.Literal(len(classes[c]))))
if distinctForPartitions:
res.add(
(part, VOID.properties, rdflib.Literal(len(classProps[c]))))
res.add((part, VOID.distinctObjects,
rdflib.Literal(len(classObjects[c]))))
for i, p in enumerate(properties):
part = rdflib.URIRef(dataset + "_property%d" % i)
res.add((dataset, VOID.propertyPartition, part))
res.add((part, RDF.type, VOID.Dataset))
res.add((part, VOID.triples, rdflib.Literal(propCount[p])))
res.add((part, VOID.properties, rdflib.Literal(1)))
res.add((part, VOID.property, p))
if distinctForPartitions:
entities = 0
propClasses = set()
for s in propSubjects[p]:
if s in typeMap:
entities += 1
for c in typeMap[s]:
propClasses.add(c)
res.add((part, VOID.entities, rdflib.Literal(entities)))
res.add((part, VOID.classes, rdflib.Literal(len(propClasses))))
res.add((part, VOID.distinctSubjects,
rdflib.Literal(len(propSubjects[p]))))
res.add((part, VOID.distinctObjects,
rdflib.Literal(len(propObjects[p]))))
return res, dataset
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