lodkit 0.5.0

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LODKit is a collection of Linked Open Data related Python functionalities.

Installation

LODKit is available on PyPI:

pip install lodkit

Usage

Triple Constructor

The lodkit.ttl triple constructor implements a Turtle-inspired functional DSL for RDF Graph generation.

lodkit.ttl aims to emulate RDF Turtle syntax by featuring Python equivalents for

• Predicate List notation
• Object List notation
• Blank Node notation
• RDF Collections

and is recursive/composable on all code paths.

lodkit.ttl implements the Iterable[lodkit.types.Triple] protocol and exposes a to_graph method for convenient construction of an rdflib.Graph instance.

Examples

The following examples show features of the lodkit.ttl triple constructor and display the equivalent RDF graph for comparison.

Predicate List notation

The lodkit.ttl constructor takes a triple subject and an arbitrary number of triple predicate-object constellations as input; this aims to emulate Turtle Predicate List notation.

The constructor accepts any RDFLib-compliant triple object in the object position, plain strings are interpreted as rdflib.Literal.

from lodkit import ttl
from rdflib import Namespace

ex = Namespace("https://example.com/")

triples = ttl(
    ex.s,
    (ex.p, ex.o),
    (ex.p2, "literal")
)

@prefix ex: <https://example.com/> .

ex:s ex:p ex:o ;
    ex:p2 "literal" .

Object List notation

Predicate-object constellation arguments in lodkit.ttl can be of arbitrary length; the first element is interpreted as triple predicate, all succeeding elements are interpreted as Turtle Object List.

triples = ttl(
    ex.s,
    (ex.p, ex.o1, ex.o2, "literal")
)

@prefix ex: <https://example.com/> .

ex:s ex:p ex:o1, ex:o2, "literal" .

Blank Node notation

Python lists (of predicate-object constellations) in the object position of predicate-object constellations are interpreted as Turtle Blank Nodes.

triples = ttl(
    ex.s,
    (
        ex.p, [
            (ex.p2, ex.o),
            (ex.p3, "1", "2")
        ]
    )
)

@prefix ex: <https://example.com/> .

ex:s ex:p [ 
	ex:p2 ex:o ;
	ex:p3 "1", "2" 
] .

RDF Collections

Python tuples in the object position of predicate-object constellations are interpreted as Turtle Collection:

triples = ttl(
    ex.s,
    (ex.p, (ex.o, "1", "2", "3"))
)

@prefix ex: <https://example.com/> .

ex:s ex:p ( ex:o "1" "2" "3" ) .

Recursion on all paths

One of the strengths of lodkit.ttl is that it is recursive on all code paths.

To demonstrate the composability of the lodkit.ttl constructor, one could e.g. define a lodkit.ttl object that has another lodkit.ttl object and a blank node with an object list and yet another lodkit.ttl object (in a single element RDF Collection) defined within an RDF Collection:

triples = ttl(
    ex.s,
    (
        ex.p,
        (
            ttl(ex.s2, (ex.p2, "1")),
            [
                (ex.p3, "2", "3"),
                (ex.p4, (ttl(ex.s3, (ex.p5, "4")),))
            ],
        ),
    ),
)

@prefix ex: <https://example.com/> .

ex:s ex:p ( 
	ex:s2 
	[ 
		ex:p3 "2", "3" ;
        ex:p4 ( ex:s3 ) 
	] 
) .

ex:s2 ex:p2 "1" .

ex:s3 ex:p5 "4" .

This is actually a relatively simple example. Triple objects in the lodkit.ttl constructor can be arbitrarily nested.

lodkit.ttl is pretty recursive! :)

Building Triple Chains

As mentioned, lodkit.ttl implements the Iterable[lodkit.types.Triple] protocol; arbitrary lodkit.ttl instances can therefore be chained to create highly modular and scalable triple generation pipelines.

A minimal example of such a (layered) triple pipeline could look like this:

class TripleGenerator:

    def triple_generator_1(self) -> Iterator[Triple]:
        if conditional:
            yield (s, p, o)
        yield from ttl(s, ...)

    # more triple generator method definitions
    ...

    def __iter__(self) -> Iterator[Triple]:
        return itertools.chain(
            self.triple_generator_1(),
            self.triple_generator_2(),
            self.triple_generator_3(),
            ...
        )

triples: Iterator[Triple] = itertools.chain(TripleGenerator(), ...)

TripleChain

LODKit provides a TripleChain class for convenient triple chain construction. Also see Building Triple Chains.

lodkit.TripeChain is a simple itertools.chain subclass that implements a fluid chain interface for arbitrary successive chaining and a to_graph method for deriving an rdflib.Graph from a given chain.

Note that, unlike lodkit.ttl, TripleChain is an Iterator and can be exhausted, e.g. by calling TripleChain.to_graph.

from collections.abc import Iterator

from lodkit import TripleChain, ttl
from lodkit.types import Triple
from rdflib import Graph, Namespace

ex = Namespace("https://example.com/")


triples = ttl(ex.s, (ex.p, "1", "2", "3"))
more_triples = ttl(ex.s, (ex.p2, [(ex.p3, ex.o)]))
yet_more_triples = ttl(ex.s, (ex.p3, ex.o))


def any_iterable_of_triples() -> Iterator[Triple]:
    yield (ex.s, ex.p, ex.o)


triple_chain = (
    TripleChain(triples, more_triples)
    .chain(yet_more_triples)
    .chain(any_iterable_of_triples())
)

ex_graph = Graph()
ex_graph.bind("ex", ex)

graph: Graph = triple_chain.to_graph(graph=ex_graph)
print(graph.serialize())

@prefix ex: <https://example.com/> .

ex:s ex:p ex:o,
        "1",
        "2",
        "3" ;
    ex:p2 [ ex:p3 ex:o ] ;
    ex:p3 ex:o .

RDF Importer

lodkit.RDFImporter is a custom importer for parsing RDF files into rdflib.Graph objects.

Assuming graphs/some_graph.ttl exists in the import path, lodkit.RDFImporter makes it possible to import the RDF file like a module:

from graphs import some_graph

type(some_graph)  # <class 'rdflib.graph.Graph'>

RDF import functionality is available after registering lodkit.RDFImporter with the import maschinery e.g by calling lodkit.enable_rdf_import.

Types

lodkit.types defines several useful types for working with RDFLib-based Python code.

URI Tools

uritools.utils

URIConstructor

The URIConstructor class provides namespaced URI constructor functionality.

A URIConstructor is initialized given a namespace. Calls to the initialized object will construct rdflib.URIRefs for that namespace.

If a hash_value argument of type str | bytes is provided, the URIRef will be generated with the sha256 hash of the hash_value argument as last URI component; else a URIRef with a unique component will be generated using UUID4.

make_uri = URIConstructor("https://example.com/")

make_uri()        # rdflib.URIRef('https://example.com/<UUID4>')
make_uri("test")  # rdflib.URIRef('https://example.com/<sha256>')

make_uri("test") == make_uri("test")  # True

Namespace Tools

NamespaceGraph

lodkit.NamespaceGraph is a simple rdflib.Graph subclass for easy and convenient namespace binding.

from lodkit import NamespaceGraph
from rdflib import Namespace

class CLSGraph(NamespaceGraph):
	crm = Namespace("http://www.cidoc-crm.org/cidoc-crm/")
	crmcls = Namespace("https://clscor.io/ontologies/CRMcls/")
	clscore = Namespace("https://clscor.io/entity/")

graph = CLSGraph()

ns_check: bool = all(
	ns in map(lambda x: x[0], graph.namespaces())
	for ns in ("crm", "crmcls", "clscore")
)

print(ns_check)  # True

ClosedOntologyNamespace, DefinedOntologyNamespace

lodkit.ClosedOntologyNamespace and lodkit.DefinedOntologyNamespace are rdflib.ClosedNamespace and rdflib.DefinedNameSpace subclasses that are able to load namespace members based on an ontology.

crm = ClosedOntologyNamespace(ontology="./CIDOC_CRM_v7.1.3.ttl")

crm.E39_Actor   # URIRef('http://www.cidoc-crm.org/cidoc-crm/E39_Actor')
crm.E39_Author  # AttributeError

class crm(DefinedOntologyNamespace):
	ontology = "./CIDOC_CRM_v7.1.3.ttl"

crm.E39_Actor   # URIRef('http://www.cidoc-crm.org/cidoc-crm/E39_Actor')
crm.E39_Author  # URIRef('http://www.cidoc-crm.org/cidoc-crm/E39_Author') + UserWarning

Note that rdflib.ClosedNamespaces are meant to be instantiated and rdflib.DefinedNameSpaces are meant to be extended, which is reflected in lodkit.ClosedOntologyNamespace and lodkit.DefinedOntologyNamespace.