Source code for rdflib.void

import collections

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

[docs]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: 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("") 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,, 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,, 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,, Literal(1))) res.add((part,, 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