Source code for rdflib.void

import collections

from rdflib.graph import Graph
from rdflib.namespace import RDF, VOID
from rdflib.term import Literal, URIRef


[docs]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