OWL reasoners can be used to check the consistency of an ontology, and to deduce new fact in the ontology, typically be reclassing Instances to new Classes depending on their relations.

Several OWL reasoners exist; Owlready includes a modified version of the HermiT reasoner, developed by the department of Computer Science of the University of Oxford, and released under the LGPL licence. HermiT is written in Java, and thus you need a Java Vitual Machine to perform reasoning in Owlready.

Setting up everything

Before performing reasoning, you need to create all Classes, Properties and Instances, and to ensure that restrictions and disjointnesses / distinctnesses have been defined too.

Here is an example creating a ‘reasoning-ready’ ontology:

>>> from owlready import *

>>> onto = Ontology("")

>>> class Drug(Thing):
...     ontology = onto
...     def take(self): print("I took a drug")

>>> class ActivePrinciple(Thing):
...     ontology = onto

>>> class has_for_active_principle(Property):
...     ontology = onto
...     domain   = [Drug]
...     range    = [ActivePrinciple]
>>> ANNOTATIONS[has_for_active_principle]["python_name"] = "active_principles"

>>> class Placebo(Drug):
...     equivalent_to = [Drug & NOT(restriction(has_for_active_principle, SOME, ActivePrinciple))]
...     def take(self): print("I took a placebo")

>>> class SingleActivePrincipleDrug(Drug):
...     equivalent_to = [Drug & restriction(has_for_active_principle, EXACTLY, 1, ActivePrinciple)]
...     def take(self): print("I took a drug with a single active principle")

>>> class DrugAssociation(Drug):
...     equivalent_to = [Drug & restriction(has_for_active_principle, MIN, 2, ActivePrinciple)]
...     def take(self): print("I took a drug with %s active principles" % len(self.active_principles))

>>> acetaminophen   = ActivePrinciple("acetaminophen")
>>> amoxicillin     = ActivePrinciple("amoxicillin")
>>> clavulanic_acid = ActivePrinciple("clavulanic_acid")

>>> AllDistinct(acetaminophen, amoxicillin, clavulanic_acid)

>>> drug1 = Drug(active_principles = [acetaminophen])
>>> drug2 = Drug(active_principles = [amoxicillin, clavulanic_acid])
>>> drug3 = Drug(active_principles = [])

>>> drug1.closed_world()
>>> drug2.closed_world()
>>> drug3.closed_world()

Running the reasoner

The reasoner is simply run by calling the .sync_reasoner() method of the ontology:

>>> onto.sync_reasoner()

Results of the automatic classification

Owlready automatically gets the results of the reasoning from HermiT and reclassifies the Classes and Instances, i.e Owlready changes the superclasses of Classes and the Classes of Instances.

>>> print("drug2 new Classes:", drug2.__class__)
drug2 new Classes: onto.DrugAssociation

>>> drug1.take()
I took a drug with a single active principle

>>> drug2.take()
I took a drug with 2 active principles

>>> drug3.take()
I took a placebo

In this example, drug1, drug2 and drug3 Classes have changed! The reasoner deduced that drug2 is an Association Drug, and that drug3 is a Placebo.

Also notice how the example combines automatic classification of OWL Classes with polymorphism on Python Classes.