Ontologies

To get started with Structured Machine Learning, the first thing required is an Ontology with Named Individuals. In this guide we show the basics of working with ontologies in Ontolearn using mainly Owlapy classes. As mentioned earlier, Owlapy once part of the project, is now moved to a separate repository and imported to Ontolearn as a package for modularization purposes. Since there is no separate documentation for owlapy, therefore, for better understanding we describe some of Owlapy classes in this guide as well. Owlapy references link to the GitHub repository, whereas Ontolearn references link to the API Documentation of Ontolearn.

We will frequently use a sample ontology to give examples. You can find it in
KGs/Family/father.owl after you download the datasets. Here is a hierarchical diagram that shows the classes and their relationships:

         Thing
           |
        Person
       /   |   
   Male  Female

It contains only one object property which is ‘hasChild’ and in total there are six persons (individuals), of which four are male and two are female.

Loading an Ontology

To load an ontology as well as to manage it, you will need an OWLOntologyManager (this is an abstract class, concrete implementation in Ontolearn is mentioned below). An ontology can be loaded using the following Python code:

from owlapy.iri import IRI
from ontolearn.base import OWLOntologyManager_Owlready2

manager = OWLOntologyManager_Owlready2()
onto = manager.load_ontology(IRI.create("file://KGs/Family/father.owl"))

First, we import the IRI class and a suitable OWLOntologyManager. To load a file from our computer, we have to reference it with an IRI. Secondly, we need the Ontology Manager. Currently, Ontolearn contains one such manager: The OWLOntologyManager_Owlready2.

Now, we can already inspect the contents of the ontology. For example, to list all individuals:

for ind in onto.individuals_in_signature():
    print(ind)

You can get the object properties in the signature:

onto.object_properties_in_signature()

For more methods, see the owlapy abstract class OWLOntology or the concrete implementation in Ontolearn OWLOntology_Owlready2.

Modifying an Ontology

Axioms in ontology serve as the basis for defining the vocabulary of a domain and for making statements about the relationships between individuals and concepts in that domain. They provide a formal and precise way to represent knowledge and allow for automated reasoning and inference. Axioms can be added, modified, or removed from an ontology, allowing the ontology to evolve and adapt as new knowledge is gained.

In owlapy we also have different axioms represented by different classes. You can check all the axioms classes here. Some frequently used axioms are:

Add a new Class

Let’s suppose you want to add a new class in our example ontology KGs/Family/father.owl It can be done as follows:

from owlapy.class_expression import OWLClass
from owlapy.owl_axiom import OWLDeclarationAxiom

iri = IRI('http://example.com/father#', 'child')
child_class = OWLClass(iri)
child_class_declaration_axiom = OWLDeclarationAxiom(child_class)

manager.add_axiom(onto, child_class_declaration_axiom)

In this example, we added the class ‘child’ to the father.owl ontology. Firstly we create an instance of OWLClass to represent the concept of ‘child’ by using an IRI. On the other side, an instance of IRI is created by passing two arguments which are the namespace of the ontology and the remainder ‘child’. To declare this new class we need an axiom of type OWLDeclarationAxiom. We simply pass the child_class to create an instance of this axiom. The final step is to add this axiom to the ontology using the OWLOntologyManager. We use the add_axiom method of the manager to add into the ontology onto the axiom child_class_declaration_axiom.

Add a new Object Property / Data Property

The idea is the same as adding a new class. Instead of OWLClass, for object properties, you can use the class OWLObjectProperty and for data properties you can use the class OWLDataProperty.

from owlapy.owl_property import OWLObjectProperty, OWLDataProperty

# adding the object property 'hasParent'
hasParent_op = OWLObjectProperty(IRI('http://example.com/father#', 'hasParent'))
hasParent_op_declaration_axiom = OWLDeclarationAxiom(hasParent_op)
manager.add_axiom(onto, hasParent_op_declaration_axiom)

# adding the data property 'hasAge' 
hasAge_dp = OWLDataProperty(IRI('http://example.com/father#', 'hasAge'))
hasAge_dp_declaration_axiom = OWLDeclarationAxiom(hasAge_dp)
manager.add_axiom(onto, hasAge_dp_declaration_axiom)

See the owlapy for more OWL entities that you can add as a declaration axiom.

Add an Assertion Axiom

To assign a class to a specific individual use the following code:

from owlapy.owl_axiom import OWLClassAssertionAxiom

individuals = list(onto.individuals_in_signature())
heinz = individuals[1]  # get the 2nd individual in the list which is 'heinz'

class_assertion_axiom = OWLClassAssertionAxiom(heinz, child_class)

manager.add_axiom(onto, class_assertion_axiom)

We have used the previous method individuals_in_signature() to get all the individuals and converted them to a list, so we can access them by using indexes. In this example, we want to assert a class axiom for the individual heinz. We have used the class OWLClassAssertionAxiom where the first argument is the ‘individual’ heinz and the second argument is the ‘class_expression’. As the class expression, we used the previously defined class child_Class. Finally, add the axiom by using add_axiom method of the OWLOntologyManager.

Let’s show one more example using a OWLDataPropertyAssertionAxiom to assign the age of 17 to heinz.

from owlapy.owl_literal import OWLLiteral
from owlapy.owl_axiom import OWLDataPropertyAssertionAxiom

literal_17 = OWLLiteral(17)
dp_assertion_axiom = OWLDataPropertyAssertionAxiom(heinz, hasAge_dp, literal_17)

manager.add_axiom(onto, dp_assertion_axiom)

OWLLiteral is a class that represents the literal values in Owlapy. We have stored the integer literal value of ‘18’ in the variable literal_17. Then we construct the OWLDataPropertyAssertionAxiom by passing as the first argument, the individual heinz, as the second argument the data property hasAge_dp, and the third argument the literal value literal_17. Finally, add it to the ontology by using add_axiom method.

Check the owlapy to see all the OWL assertion axioms that you can use.

Remove an Axiom

To remove an axiom you can use the remove_axiom method of the ontology manager as follows:

manager.remove_axiom(onto,dp_assertion_axiom)

The first argument is the ontology you want to remove the axiom from and the second argument is the axiom you want to remove.

Save an Ontology

If you modified an ontology, you may want to save it as a new file. To do this you can use the save_ontology method of the OWLOntologyManager. It requires two arguments, the first is the ontology you want to save and The second is the IRI of the new ontology.

manager.save_ontology(onto, IRI.create('file:/' + 'test' + '.owl'))

The above line of code will save the ontology onto in the file test.owl which will be created in the same directory as the file you are running this code.

Worlds

Owlready2 stores every triple in a ‘World’ object, and it can handle several Worlds in parallel. Owlready2 uses an optimized quadstore to store the world. Each world object is stored in a separate quadstore and by default the quadstore is stored in memory, but it can also be stored in an SQLite3 file. The method save_world() of the ontology manager does the latter. When an OWLOntologyManager object is created, a new world is also created as an attribute of the manager. By calling the method load_ontology(iri) the ontology is loaded to this world.

It possible to create several isolated “worlds”, sometimes called “universe of speech”. This makes it possible in particular to load the same ontology several times, independently, that is to say, without the modifications made on one copy affecting the other copy. Sometimes the need to isolate an ontology arise. What that means is that you can have multiple reference of the same ontology in different worlds.


In the next guide we will explore the KnowledgeBase class that is needed to run a concept learner.