lodkit 0.4.0

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LODKit

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[_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[_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, _Triple, ttl
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 importing RDF files as if they were modules.

Assuming 'graphs/some_graph.ttl' exists in the import path, lodkit.RDFImporter makes it possible to do the following:

import lodkit
from graphs import some_graph

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

Note that lodkit.RDFImporter is available on import lodkit.

Types

lodkit.lod_types defines several useful typing.TypeAliases and typing.Literals for working with RDFLib-based Python functionalities.

URI Tools

uriclass, make_uriclass

uriclass and make_uriclass provide dataclass-inspired URI constructor functionality.

With uriclass, class-level attributes are converted to URIs according to uri_constructor. For class attributes with just type information, URIs are constructed using UUIDs, for class attributes with string values, URIs are constructed using hashing based on that string.

from lodkit import uriclass

@uriclass(Namespace("https://test.org/test/"))
class uris:
    x1: str

    y1 = "hash value 1"
    y2 = "hash value 1"

    print(uris.x1)             # Namespace("https://test.org/test/<UUID>")
    print(uris.y1 == uris.y2)  # True

make_uriclass provides equalent functionality but is more apt for dynamic use.

from lodkit import make_uriclass

uris = make_uriclass(
    cls_name="TestURIFun",
	    namespace="https://test.org/test/",
        fields=("x", ("y1", "hash value 1"), ("y2", "hash value 1")),
    )

    print(uris.x1)             # Namespace("https://test.org/test/<UUID>")
    print(uris.y1 == uris.y2)  # True

uritools.utils

uritools.utils defines base functionality for generating UUID-based and hashed URIs.

URIConstructorFactory (alias of mkuri_factory) constructs a callable for generating URIs. The returned callable takes an optional str argument 'hash_value'; If a hash value is given, the segment is generated using a hash function, else the path is generated using a uuid.

from lodkit import URIConstructorFactory

mkuri = URIConstructorFactory("https://test.namespace/")
print(mkuri())                         # URIRef("https://test.namespace/<UUID>")
print(mkuri("test") == mkuri("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.

Testing Tools

lodkit.testing_tools aims to provide general definitions (e.g Graph format options) and Hypothesis strategies useful for testing RDFLib-based Python and code.

E.g. the TripleStrategies.triples strategy generates random triples utilizing all permissible subject, predicate and object types including lang-tagged and xsd-typed literals. The following uses the triples strategies together with a Hypothesis strategy to create random graphs:

from hypothesis import given, strategies as st
from lodkit import tst
from rdflib import Graph


@given(triples=st.lists(tst.triples, min_size=1, max_size=10))
def test_some_function(triples):
    graph = Graph()
    for triple in triples:
        graph.add(triple)

    assert len(graph) == len(triples)

The strategy generates up to 100 (by default, see settings) lists of 1-10 tuple[_TripleSubject, URIRef, _TripleObject] and passes them to the test function.

Warning: The API of lodkit.tesing_tools is very likely to change soon! Strategies should be module-level callables and not properties of a Singleton.