Source code for rdfextras.utils.graphutils

import collections
import rdflib
from rdflib import RDF, RDFS

"""
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))


[docs]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==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
[docs]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==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