1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
|
import collections
from rdflib import URIRef, Graph, Literal
from rdflib.namespace import VOID, RDF
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 = 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()
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
|
