Creating RDF triples

Creating Nodes

RDF data is a graph where the nodes are URI references, Blank Nodes or Literals. In RDFLib, these node types are represented by the classes URIRef, BNode, and Literal. URIRefs and BNodes can both be thought of as resources, such a person, a company, a website, etc.

  • A BNode is a node where the exact URI is not known.

  • A URIRef is a node where the exact URI is knonw. URIRefs are also used to represent the properties/predicates in the RDF graph.

  • Literals represent attribute values, such as a name, a date, a number, etc. The most common literal values are XML data types, e.g. string, int…

Nodes can be created by the constructors of the node classes:

from rdflib import URIRef, BNode, Literal

bob = URIRef("http://example.org/people/Bob")
linda = BNode()  # a GUID is generated

name = Literal('Bob')  # passing a string
age = Literal(24)  # passing a python int
height = Literal(76.5)  # passing a python float

Literals can be created from Python objects, this creates data-typed literals, for the details on the mapping see Literals.

For creating many URIRefs in the same namespace, i.e. URIs with the same prefix, RDFLib has the rdflib.namespace.Namespace class:

from rdflib import Namespace

n = Namespace("http://example.org/people/")

n.bob  # = rdflib.term.URIRef(u'http://example.org/people/bob')
n.eve  # = rdflib.term.URIRef(u'http://example.org/people/eve')

This is very useful for schemas where all properties and classes have the same URI prefix. RDFLib defines Namespaces for some common RDF/OWL schemas, including most W3C ones:

from rdflib.namespace import CSVW, DC, DCAT, DCTERMS, DOAP, FOAF, ODRL2, ORG, OWL, \
                           PROF, PROV, RDF, RDFS, SDO, SH, SKOS, SOSA, SSN, TIME, \
                           VOID, XMLNS, XSD

RDF.type
# = rdflib.term.URIRef("http://www.w3.org/1999/02/22-rdf-syntax-ns#type")

FOAF.knows
# = rdflib.term.URIRef("http://xmlns.com/foaf/0.1/knows")

PROF.isProfileOf
# = rdflib.term.URIRef("http://www.w3.org/ns/dx/prof/isProfileOf")

SOSA.Sensor
# = rdflib.term.URIRef("http://www.w3.org/ns/sosa/Sensor")

Adding Triples

We already saw in Loading and saving RDF, how triples can be added from files and online locations with with the parse() function.

Triples can also be added within Python code directly, using the add() function:

Graph.add(triple)[source]

Add a triple with self as context

add() takes a 3-tuple (a “triple”) of RDFLib nodes. Try the following with the nodes and namespaces we defined previously:

from rdflib import Graph
g = Graph()
g.bind("foaf", FOAF)

g.add((bob, RDF.type, FOAF.Person))
g.add((bob, FOAF.name, name))
g.add((bob, FOAF.knows, linda))
g.add((linda, RDF.type, FOAF.Person))
g.add((linda, FOAF.name, Literal("Linda")))

print(g.serialize(format="turtle").decode("utf-8"))

outputs:

@prefix foaf: <http://xmlns.com/foaf/0.1/> .

<http://example.org/people/Bob> a foaf:Person ;
    foaf:knows [ a foaf:Person ;
            foaf:name "Linda" ] ;
    foaf:name "Bob" .

For some properties, only one value per resource makes sense (i.e they are functional properties, or have max-cardinality of 1). The set() method is useful for this:

g.add((bob, FOAF.age, Literal(42)))
print("Bob is ", g.value(bob, FOAF.age))
# prints: Bob is 42

g.set((bob, FOAF.age, Literal(43)))  # replaces 42 set above
print("Bob is now ", g.value(bob, FOAF.age))
# prints: Bob is now 43

rdflib.graph.Graph.value() is the matching query method, it will return a single value for a property, optionally raising an exception if there are more.

You can also add triples by combining entire graphs, see Set Operations on RDFLib Graphs.

Removing Triples

Similarly, triples can be removed by a call to remove():

Graph.remove(triple)[source]

Remove a triple from the graph

If the triple does not provide a context attribute, removes the triple from all contexts.

When removing, it is possible to leave parts of the triple unspecified (i.e. passing None), this will remove all matching triples:

g.remove((bob, None, None))  # remove all triples about bob

An example

LiveJournal produces FOAF data for their users, but they seem to use foaf:member_name for a person’s full name but foaf:member_name isn’t in FOAF’s namespace and perhaps they should have used foaf:name

To retrieve some LiveJournal data, add a foaf:name for every foaf:member_name and then remove the foaf:member_name values to ensure the data actually aligns with other FOAF data, we could do this:

from rdflib import Graph
from rdflib.namespace import FOAF

g = Graph()
# get the data
g.parse("http://danbri.livejournal.com/data/foaf")

# for every foaf:member_name, add foaf:name and remove foaf:member_name
for s, p, o in g.triples((None, FOAF['member_name'], None)):
    g.add((s, FOAF['name'], o))
    g.remove((s, FOAF['member_name'], o))

Note

Since rdflib 5.0.0, using foaf:member_name is somewhat prevented in RDFlib since FOAF is declared as a ClosedNamespace() class instance that has a closed set of members and foaf:member_name isn’t one of them! If LiveJournal used RDFlib 5.0.0, an error would have been raised for foaf:member_name when the triple was created.

Creating Containers & Collections

There are two convenience classes for RDF Containers & Collections which you can use instead of declaring each triple of a Containers or a Collections individually:

See their documentation for how.