Navigating Graphs |
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An RDF Graph is a set of RDF triples, and we try to mirror exactly this in RDFLib. The Python |
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Graphs as Iterators |
RDFLib graphs override |
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This loop iterates through all the subjects(s), predicates (p) & objects (o) in |
Contains check |
Graphs implement ~rdflib.graph.Graph.__contains__ , so you can check if a triple is in a graph with a |
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Note that this triple does not have to be completely bound: |
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Set Operations on RDFLib Graphs |
Graphs override several pythons operators: ~rdflib.graph.Graph.__iadd__ , ~rdflib.graph.Graph.__isub__ , |
etc. This supports addition, subtraction and other set-operations on Graphs: |
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operation effect |
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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.
Instead of iterating through all triples, RDFLib graphs support basic triple pattern matching with a ~rdflib.graph.Graph.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 ~rdflib.graph.Graph.objects
, ~rdflib.graph.Graph.subjects
, ~rdflib.graph.Graph.predicates
, ~rdflib.graph.Graph.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 ~rdflib.graph.Graph.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)
Here is a list of all convenience methods for querying Graphs:
rdflib.graph.Graph.triples
rdflib.graph.Graph.value
rdflib.graph.Graph.subjects
rdflib.graph.Graph.objects
rdflib.graph.Graph.predicates
rdflib.graph.Graph.subject_objects
rdflib.graph.Graph.subject_predicates
rdflib.graph.Graph.predicate_objects