I've been using RDFLib to parse Job posts extracted from Common Crawl. RDF Literals It automatically parses XML Schema Datatypes into Python datastructures, but doesn't handle the <http://schema.org/Date> datatype that commonly occurs in JSON-LD. It's easy to add with the rdflib.term.bind command, but this kind of global binding could lead to problems.

When RDFLib parses a literal it will create a rdflib.term.Literal object and the value field will contain the Python type if it can be successfully converted, otherwise it will be None. This object has a toPython() method that will return the value unless it is None, in which case it will return the object itself. To see how this works here's some simple code to parse some RDF data and output all the objects: both in raw form, through toPython and the value.

def parse_objects(data, format='ntriples'):
G = rdflib.Graph()
G.parse(data=data, format=format)
return [(o, o.toPython(), o.value) for o in G.objects()]

For a string literal it is represented as the string itself.

parse_objects('_:b <http://example.org/value> "1" . \n')

>> [(rdflib.term.Literal('1'), '1', '1')]

For an XML Schema integer it is stored as a Python integer

parse_objects('_:b <http://example.org/value> "1"^^<http://www.w3.org/2001/XMLSchema#integer> . \n')

>> [(rdflib.term.Literal('1', datatype=rdflib.term.URIRef('http://www.w3.org/2001/XMLSchema#integer')),
>>>  1, 1)]

Note that if the data doesn't match the type the value remains as None.

parse_objects('_:b <http://example.org/value> "2020-01-01"^^<http://www.w3.org/2001/XMLSchema#integer> . \n')

>> [(rdflib.term.Literal('2020-01-01', datatype=rdflib.term.URIRef('http://www.w3.org/2001/XMLSchema#integer')),
>>>  rdflib.term.Literal('2020-01-01', datatype=rdflib.term.URIRef('http://www.w3.org/2001/XMLSchema#integer')),
>>>  None)]

If we have a custom datatype then the value also remains as None.

parse_objects('_:b <http://example.org/value> "2020-01-01"^^<http://schema.org/Date> . \n')

>> [(rdflib.term.Literal('2020-01-01', datatype=rdflib.term.URIRef('http://schema.org/Date')),
>>>  rdflib.term.Literal('2020-01-01', datatype=rdflib.term.URIRef('http://schema.org/Date')),
>>>  None)]

We can add a custom datatype with rdflib.term.bind that allows converting between Python types and RDF types. In this case we're only interested in converting from RDF to Python. The arguments are:

• datatype: The RDF Datatype we want to convert
• pythontype: The corresponding Python datatype
• constructor: How to turn an RDF literal to a Python datatype
• lexicalizer: How to turn a Python datatype to an RDF (not needed here)
• datatype_specific: Whether the binding is specific or general; there are other representations of datetime so set to True
import datetime, dateutil
rdflib.term.bind(datatype=rdflib.URIRef('http://schema.org/Date'),
pythontype=datetime.datetime,
constructor=dateutil.parser.isoparse,
lexicalizer= lambda dt: dt.isoformat(),
datatype_specific=True)

Then running the exact same code now gives a different result, with the correct type.

parse_objects('_:b <http://example.org/value> "2020-01-01"^^<http://schema.org/Date> . \n')

>> [(rdflib.term.Literal('2020-01-01T00:00:00', datatype=rdflib.term.URIRef('http://schema.org/Date')),
>>>  datetime.datetime(2020, 1, 1, 0, 0),
>>>  datetime.datetime(2020, 1, 1, 0, 0))]

As for native types if it parsing would be an error we get the value still being null:

parse_objects('_:b <http://example.org/value> "1"^^<http://schema.org/Date> . \n')

>> [(rdflib.term.Literal('1', datatype=rdflib.term.URIRef('http://schema.org/Date')),
>>>  rdflib.term.Literal('1', datatype=rdflib.term.URIRef('http://schema.org/Date')),
>>>  None)]

I really don't like that running the same inputs gives a different output for a parser. It can be really hard to reason about what is happening, as this could be set deep in some code. Even worse if another package I import uses a different binding for this RDF I could break it. Ideally bindings would be passed in some scope (e.g. using an object), rather than global state. Since not many things use RDF in practice it's not a big issue, but it is not a robust design - if this was used in YAML parsing it could be catastrophic.