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Navigating Graphs

An RDF Graph is a set of RDF triples, and we try to mirror exactly this in RDFLib. The Python Graph tries to emulate a container type.

Graphs as Iterators

RDFLib graphs override __iter__() in order to support iteration over the contained triples:

for s, p, o in someGraph:
    if not (s, p, o) in someGraph:
        raise Exception("Iterator / Container Protocols are Broken!!")

This loop iterates through all the subjects(s), predicates (p) & objects (o) in someGraph.

Contains check

Graphs implement __contains__(), so you can check if a triple is in a graph with a triple in graph syntax:

from rdflib import URIRef
from rdflib.namespace import RDF

bob = URIRef("http://example.org/people/bob")
if (bob, RDF.type, FOAF.Person) in graph:
    print("This graph knows that Bob is a person!")

Note that this triple does not have to be completely bound:

if (bob, None, None) in graph:
    print("This graph contains triples about Bob!")

Set Operations on RDFLib Graphs

Graphs override several pythons operators: __iadd__(), __isub__(), etc. This supports addition, subtraction and other set-operations on Graphs:

operation effect
G1 + G2 return new graph with union (triples on both)
G1 += G2 in place union / addition
G1 - G2 return new graph with difference (triples in G1, not in G2)
G1 -= G2 in place difference / subtraction
G1 & G2 intersection (triples in both graphs)
G1 ^ G2 xor (triples in either G1 or G2, but not in both)

Warning

Set-operations on graphs assume Blank Nodes are shared between graphs. This may or may not be what you want. See merging for details.

Basic Triple Matching

Instead of iterating through all triples, RDFLib graphs support basic triple pattern matching with a triples() function. This function is a generator of triples that match a pattern given by arguments, i.e. arguments restrict the triples that are returned. Terms that are None are treated as a wildcard. For example:

g.parse("some_foaf.ttl")
# find all subjects (s) of type (rdf:type) person (foaf:Person)
for s, p, o in g.triples((None, RDF.type, FOAF.Person)):
    print(f"{s} is a person")

# find all subjects of any type
for s, p, o in g.triples((None,  RDF.type, None)):
    print(f"{s} is a {o}")

# create a graph
bobgraph = Graph()
# add all triples with subject 'bob'
bobgraph += g.triples((bob, None, None))

If you are not interested in whole triples, you can get only the bits you want with the methods objects(), subjects(), predicates(), predicate_objects(), etc. Each take parameters for the components of the triple to constraint:

for person in g.subjects(RDF.type, FOAF.Person):
    print("{} is a person".format(person))

Finally, for some properties, only one value per resource makes sense (i.e they are functional properties, or have a max-cardinality of 1). The value() method is useful for this, as it returns just a single node, not a generator:

# get any name of bob
name = g.value(bob, FOAF.name)
# get the one person that knows bob and raise an exception if more are found
person = g.value(predicate=FOAF.knows, object=bob, any=False)

Graph methods for accessing triples

Here is a list of all convenience methods for querying Graphs: