updated to owlapy 1.3.0

README.md

@@ -1,12 +1,12 @@
 [![Coverage](https://img.shields.io/badge/coverage-95%25-green)](https://ontolearn-docs-dice-group.netlify.app/usage/09_further_resources#code-coverage)
-[![Pypi](https://img.shields.io/badge/pypi-0.7.3-blue)](https://pypi.org/project/ontolearn/0.7.3/)
-[![Docs](https://img.shields.io/badge/documentation-0.7.3-yellow)](https://ontolearn-docs-dice-group.netlify.app/usage/01_introduction)
+[![Pypi](https://img.shields.io/badge/pypi-0.7.4-blue)](https://pypi.org/project/ontolearn/0.7.4/)
+[![Docs](https://img.shields.io/badge/documentation-0.7.4-yellow)](https://ontolearn-docs-dice-group.netlify.app/usage/01_introduction)
 [![Python](https://img.shields.io/badge/python-3.10.13+-4584b6)](https://www.python.org/downloads/release/python-31013/)
  
 
 ![Ontolearn](docs/_static/images/Ontolearn_logo.png)
 
-# Ontolearn: Learning OWL Class Expression
+# Ontolearn: Learning OWL Class Expressions
 
 *Ontolearn* is an open-source software library for learning owl class expressions at large scale.
 

docs/usage/01_introduction.md

@@ -1,6 +1,6 @@
 # About Ontolearn
 
-**Version:** ontolearn 0.7.3
+**Version:** ontolearn 0.7.4
 
 **GitHub repository:** [https://github.com/dice-group/Ontolearn](https://github.com/dice-group/Ontolearn)
 

examples/example_reasoner.py

@@ -118,14 +118,14 @@
 onto = kb.ontology
 manager = onto.get_owl_ontology_manager()
 
-manager.add_axiom(onto, OWLEquivalentObjectPropertiesAxiom([r6, r5]))
-manager.add_axiom(onto, OWLEquivalentObjectPropertiesAxiom([r5, r6]))
-manager.add_axiom(onto, OWLObjectPropertyDomainAxiom(r1, ST))
+onto.add_axiom(OWLEquivalentObjectPropertiesAxiom([r6, r5]))
+onto.add_axiom(OWLEquivalentObjectPropertiesAxiom([r5, r6]))
+onto.add_axiom(OWLObjectPropertyDomainAxiom(r1, ST))
 
-manager.add_axiom(onto, OWLSubClassOfAxiom(R, r5Q))
-manager.add_axiom(onto, OWLSubClassOfAxiom(ST, U))
+onto.add_axiom(OWLSubClassOfAxiom(R, r5Q))
+onto.add_axiom(OWLSubClassOfAxiom(ST, U))
 
-# manager.save_ontology(onto, IRI.create('file:/' + 'test' + '.owl'))
+# onto.save(IRI.create('file:/' + 'test' + '.owl'))
 
 base_reasoner = OntologyReasoner(onto)
 # reasoner = FastInstanceCheckerReasoner(

ontolearn/__init__.py

@@ -22,4 +22,4 @@
 # SOFTWARE.
 # -----------------------------------------------------------------------------
 
-__version__ = '0.7.3'
+__version__ = '0.7.4'

ontolearn/base_concept_learner.py

@@ -303,7 +303,7 @@ def predict(self, individuals: List[OWLNamedIndividual],
             ontology: OWLOntology = cast(Ontology, self.kb.ontology)
             manager: OWLOntologyManager = ontology.get_owl_ontology_manager()
             for axiom in axioms:
-                manager.add_axiom(ontology, axiom)
+                ontology.add_axiom(axiom)
             if reasoner is None:
                 reasoner = FastInstanceCheckerReasoner(ontology, base_reasoner=OntologyReasoner(ontology))
 
@@ -322,9 +322,9 @@ def predict(self, individuals: List[OWLNamedIndividual],
         # Remove the axioms from the ontology
         if axioms is not None:
             for axiom in axioms:
-                manager.remove_axiom(ontology, axiom)
+                ontology.remove_axiom(axiom)
             for ind in individuals:
-                manager.remove_axiom(ontology, OWLDeclarationAxiom(ind))
+                ontology.remove_axiom(OWLDeclarationAxiom(ind))
 
         return predictions
 
@@ -362,7 +362,7 @@ def save_best_hypothesis(self, n: int = 10, path: str = 'Predictions', rdf_forma
         for ith, h in enumerate(self.best_hypotheses(n=n)):
             cls_a: OWLClass = OWLClass(IRI.create(NS, "Pred_" + str(ith)))
             equivalent_classes_axiom = OWLEquivalentClassesAxiom([cls_a, h])
-            manager.add_axiom(ontology, equivalent_classes_axiom)
+            ontology.add_axiom(equivalent_classes_axiom)
             # @TODO:CD: We should find a way to include information (F1score etc) outside of OWL class expression instances
             """
             try:
@@ -373,14 +373,14 @@ def save_best_hypothesis(self, n: int = 10, path: str = 'Predictions', rdf_forma
             if isinstance(self.quality_func, Accuracy):
                 accuracy = OWLAnnotationAssertionAxiom(cls_a.iri, OWLAnnotation(
                     OWLAnnotationProperty(IRI.create(SNS, "accuracy")), OWLLiteral(quality)))
-                manager.add_axiom(ontology, accuracy)
+                ontology.add_axiom(accuracy)
             elif isinstance(self.quality_func, F1):
                 f1_score = OWLAnnotationAssertionAxiom(cls_a.iri, OWLAnnotation(
                     OWLAnnotationProperty(IRI.create(SNS, "f1_score")), OWLLiteral(quality)))
-                manager.add_axiom(ontology, f1_score)
+                ontology.add_axiom(f1_score)
             """
 
-        manager.save_ontology(ontology, IRI.create('file:/' + path + '.owl'))
+        ontology.save(IRI.create('file:/' + path + '.owl'))
 
     def load_hypotheses(self, path: str) -> Iterable[OWLClassExpression]:
         """

ontolearn/learning_problem_generator.py

@@ -104,7 +104,7 @@ def export_concepts(self, concepts: List[Node], path: str):
         for ith, h in enumerate(concepts):
             cls_a: OWLClass = OWLClass(IRI.create(NS, "Pred_" + str(ith)))
             equivalent_classes_axiom = OWLEquivalentClassesAxiom([cls_a, h.concept])
-            manager.add_axiom(ontology, equivalent_classes_axiom)
+            ontology.add_axiom(equivalent_classes_axiom)
 
             count = None
             try:
@@ -118,9 +118,9 @@ def export_concepts(self, concepts: List[Node], path: str):
             if count is not None:
                 num_inds = OWLAnnotationAssertionAxiom(cls_a.iri, OWLAnnotation(
                     OWLAnnotationProperty(IRI.create(SNS, "covered_inds")), OWLLiteral(count)))
-                manager.add_axiom(ontology, num_inds)
+                ontology.add_axiom(num_inds)
 
-        manager.save_ontology(ontology, IRI.create('file:/' + path + '.owl'))
+        ontology.save(IRI.create('file:/' + path + '.owl'))
 
     def concept_individuals_to_string_balanced_examples(self, concept: OWLClassExpression) -> Dict[str, Set]:
 

ontolearn/utils/static_funcs.py

@@ -311,7 +311,7 @@ def save_owl_class_expressions(expressions: Union[OWLClassExpression, List[OWLCl
         cls_a = OWLClass(IRI.create(NS, str(th)))
         equivalent_classes_axiom = OWLEquivalentClassesAxiom([cls_a, i])
         try:
-            manager.add_axiom(ontology, equivalent_classes_axiom)
+            ontology.add_axiom(equivalent_classes_axiom)
         except AttributeError:
             print(traceback.format_exc())
             print("Exception at creating OWLEquivalentClassesAxiom")
@@ -320,7 +320,7 @@ def save_owl_class_expressions(expressions: Union[OWLClassExpression, List[OWLCl
             print(i)
             print(expressions)
             exit(1)
-    manager.save_ontology(ontology, IRI.create('file:/' + path + '.owl'))
+    ontology.save(IRI.create('file:/' + path + '.owl'))
 
 
 def verbalize(predictions_file_path: str):  # pragma: no cover

setup.py

@@ -46,7 +46,7 @@
     "tqdm>=4.64.0",
     "transformers>=4.38.1",
     "pytest>=7.2.2",
-    "owlapy==1.2.1",
+    "owlapy==1.3.0",
     "dicee==0.1.4",
     "ontosample>=0.2.2",
     "sphinx>=7.2.6",
@@ -94,7 +94,7 @@ def deps_list(*pkgs):
 setup(
     name="ontolearn",
     description="Ontolearn is an open-source software library for structured machine learning in Python. Ontolearn includes modules for processing knowledge bases, inductive logic programming and ontology engineering.",
-    version="0.7.3",
+    version="0.7.4",
     packages=find_packages(),
     install_requires=extras["min"],
     extras_require=extras,

tests/test_knowledge_base.py

@@ -143,13 +143,13 @@ def test_property_retrieval(self):
 
         # indirect object property values of an individual
         super_in_bond = OWLObjectProperty(IRI.create(self.namespace, 'super_inBond'))
-        self.mgr.add_axiom(self.onto, OWLSubObjectPropertyOfAxiom(self.in_bond, super_in_bond))
+        self.onto.add_axiom(OWLSubObjectPropertyOfAxiom(self.in_bond, super_in_bond))
         inds = frozenset(self.kb.get_object_property_values(self.bond5225, super_in_bond, direct=False))
         true_inds = {self.d91_32, self.d91_17}
         self.assertEqual(true_inds, inds)
         inds = frozenset(self.kb.get_object_property_values(self.bond5225, super_in_bond, direct=True))
         self.assertEqual(frozenset(), inds)
-        self.mgr.remove_axiom(self.onto, OWLSubObjectPropertyOfAxiom(self.in_bond, super_in_bond))
+        self.onto.remove_axiom(OWLSubObjectPropertyOfAxiom(self.in_bond, super_in_bond))
 
         # data property values of an individual
         values = frozenset(self.kb.get_data_property_values(self.d91_32, self.charge, direct=True))
@@ -158,43 +158,43 @@ def test_property_retrieval(self):
 
         # indirect data property values of an individual
         super_charge = OWLDataProperty(IRI.create(self.namespace, 'super_charge'))
-        self.mgr.add_axiom(self.onto, OWLSubDataPropertyOfAxiom(self.charge, super_charge))
+        self.onto.add_axiom(OWLSubDataPropertyOfAxiom(self.charge, super_charge))
         values = frozenset(self.kb.get_data_property_values(self.d91_32, super_charge, direct=False))
         true_values = {OWLLiteral(0.146)}
         self.assertEqual(true_values, values)
         values = frozenset(self.kb.get_data_property_values(self.d91_32, super_charge, direct=True))
         self.assertEqual(frozenset(), values)
-        self.mgr.remove_axiom(self.onto, OWLSubDataPropertyOfAxiom(self.charge, super_charge))
+        self.onto.remove_axiom(OWLSubDataPropertyOfAxiom(self.charge, super_charge))
 
         # object properties of an individual
         properties = frozenset(self.kb.get_object_properties_for_ind(self.bond5225, direct=True))
         true_properties = {self.in_bond}
         self.assertEqual(true_properties, properties)
 
         # indirect object properties of an individual
-        self.mgr.add_axiom(self.onto, OWLSubObjectPropertyOfAxiom(self.in_bond, self.has_bond))
+        self.onto.add_axiom(OWLSubObjectPropertyOfAxiom(self.in_bond, self.has_bond))
         properties = frozenset(self.kb.get_object_properties_for_ind(self.bond5225, direct=False))
         true_properties = {self.in_bond, self.has_bond}
         self.assertEqual(true_properties, properties)
         properties = frozenset(self.kb.get_object_properties_for_ind(self.bond5225, direct=True))
         true_properties = {self.in_bond}
         self.assertEqual(true_properties, properties)
-        self.mgr.remove_axiom(self.onto, OWLSubObjectPropertyOfAxiom(self.in_bond, self.has_bond))
+        self.onto.remove_axiom(OWLSubObjectPropertyOfAxiom(self.in_bond, self.has_bond))
 
         # data properties of an individual
         properties = frozenset(self.kb.get_data_properties_for_ind(self.d91_32, direct=True))
         true_properties = {self.charge}
         self.assertEqual(true_properties, properties)
 
         # indirect data properties of an individual
-        self.mgr.add_axiom(self.onto, OWLSubDataPropertyOfAxiom(self.charge, self.act))
+        self.onto.add_axiom(OWLSubDataPropertyOfAxiom(self.charge, self.act))
         properties = frozenset(self.kb.get_data_properties_for_ind(self.d91_32, direct=False))
         true_properties = {self.charge, self.act}
         self.assertEqual(true_properties, properties)
         properties = frozenset(self.kb.get_data_properties_for_ind(self.d91_32, direct=True))
         true_properties = {self.charge}
         self.assertEqual(true_properties, properties)
-        self.mgr.remove_axiom(self.onto, OWLSubDataPropertyOfAxiom(self.charge, self.act))
+        self.onto.remove_axiom(OWLSubDataPropertyOfAxiom(self.charge, self.act))
 
     def test_ignore(self):
         concepts_to_ignore = {self.bond1, self.compound}