LODKit

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

Installation

Usage

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

Triple Tools

Triple tools (so far only) defines lodkit.ttl, a triple constructor implementing a Turtle-like interface.

lodkit.ttl aims to implement turtle predicate list notation by taking a triple subject and predicate-object pairs; objects in a predicate-object pair can be

• objects of type lodkit._TripleObject (strings are also permissible and are interpreted as rdflib.Literal),
• tuples of lodkit._TripleObject (see turtle object lists),
• lists of predicate-object pairs, emulating turtle blank node notation.
• lodkit.ttl objects.

from collections.abc import Iterator

from lodkit import _Triple, ttl
from rdflib import Graph, Literal, RDF, RDFS, URIRef


triples: Iterator[_Triple] = ttl(
    URIRef("https://subject"),
    (RDF.type, URIRef("https://some_type")),
    (RDFS.label, (Literal("label 1"), "label 2")),
    (RDFS.seeAlso, [(RDFS.label, "label 3")]),
    (
        RDFS.isDefinedBy,
        ttl(URIRef("https://subject_2"), (RDF.type, URIRef("https://another_type"))),
    ),
)

graph: Graph = triples.to_graph()

The above graph serialized to turtle:

@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .

<https://subject> a <https://some_type> ;
    rdfs:label "label 1",
        "label 2" ;
    rdfs:isDefinedBy <https://subject_2> ;
    rdfs:seeAlso [ rdfs:label "label 3" ] .

<https://subject_2> a <https://another_type> .

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.