OWLAPY

OWLAPY is a Python Framework for creating and manipulating OWL Ontologies.

Have a look at the Documentation.

DeepWiki can also help you get started with owlapy.

Installation

Installation from Source

git clone https://github.com/dice-group/owlapy
conda create -n temp_owlapy python=3.10.13 --no-default-packages && conda activate temp_owlapy && pip3 install -e .

or

pip3 install owlapy

# To download RDF knowledge graphs
wget https://files.dice-research.org/projects/Ontolearn/KGs.zip -O ./KGs.zip && unzip KGs.zip
pytest -p no:warnings -x # Running  147 tests ~ 35 secs

Examples

OWL Reasoning from Command line

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owlapy --path_ontology "KGs/Family/family-benchmark_rich_background.owl" --inference_types "all" --out_ontology "enriched_family.owl"

--inference_types can be specified by selecting one from

["InferredClassAssertionAxiomGenerator",
"InferredSubClassAxiomGenerator",
"InferredDisjointClassesAxiomGenerator",
"InferredEquivalentClassAxiomGenerator",
"InferredEquivalentDataPropertiesAxiomGenerator",
"InferredEquivalentObjectPropertyAxiomGenerator",
"InferredInverseObjectPropertiesAxiomGenerator",
"InferredSubDataPropertyAxiomGenerator",
"InferredSubObjectPropertyAxiomGenerator",
"InferredDataPropertyCharacteristicAxiomGenerator",
"InferredObjectPropertyCharacteristicAxiomGenerator"]

Exploring OWL Ontology

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from owlapy.owl_ontology import SyncOntology

ontology_path = "KGs/Family/father.owl"
onto = SyncOntology(ontology_path)

print({owl_class.remainder for owl_class in onto.classes_in_signature()}) 
# {'Thing', 'female', 'male', 'person'}

print({individual.remainder for individual in onto.individuals_in_signature()}) 
# {'michelle', 'stefan', 'martin', 'anna', 'heinz', 'markus'}

print({object_property.remainder for object_property in onto.object_properties_in_signature()})
# {'hasChild'}

for owl_subclass_of_axiom in onto.get_tbox_axioms():
    print(owl_subclass_of_axiom)

# OWLEquivalentClassesAxiom([OWLClass(IRI('http://example.com/father#', 'male')), OWLObjectComplementOf(OWLClass(IRI('http://example.com/father#', 'female')))],[])
# OWLSubClassOfAxiom(sub_class=OWLClass(IRI('http://example.com/father#', 'female')),super_class=OWLClass(IRI('http://example.com/father#', 'person')),annotations=[])
# OWLSubClassOfAxiom(sub_class=OWLClass(IRI('http://example.com/father#', 'male')),super_class=OWLClass(IRI('http://example.com/father#', 'person')),annotations=[])
# OWLSubClassOfAxiom(sub_class=OWLClass(IRI('http://example.com/father#', 'person')),super_class=OWLClass(IRI('http://www.w3.org/2002/07/owl#', 'Thing')),annotations=[])
# OWLObjectPropertyRangeAxiom(OWLObjectProperty(IRI('http://example.com/father#', 'hasChild')),OWLClass(IRI('http://example.com/father#', 'person')),[])
# OWLObjectPropertyDomainAxiom(OWLObjectProperty(IRI('http://example.com/father#', 'hasChild')),OWLClass(IRI('http://example.com/father#', 'person')),[])


for axiom in onto.get_abox_axioms():
    print(axiom)
    
# OWLClassAssertionAxiom(individual=OWLNamedIndividual(IRI('http://example.com/father#', 'anna')),class_expression=OWLClass(IRI('http://example.com/father#', 'female')),annotations=[])
# OWLClassAssertionAxiom(individual=OWLNamedIndividual(IRI('http://example.com/father#', 'michelle')),class_expression=OWLClass(IRI('http://example.com/father#', 'female')),annotations=[])
# OWLClassAssertionAxiom(individual=OWLNamedIndividual(IRI('http://example.com/father#', 'martin')),class_expression=OWLClass(IRI('http://example.com/father#', 'male')),annotations=[])
# OWLClassAssertionAxiom(individual=OWLNamedIndividual(IRI('http://example.com/father#', 'markus')),class_expression=OWLClass(IRI('http://example.com/father#', 'male')),annotations=[])
# OWLClassAssertionAxiom(individual=OWLNamedIndividual(IRI('http://example.com/father#', 'heinz')),class_expression=OWLClass(IRI('http://example.com/father#', 'male')),annotations=[])
# OWLClassAssertionAxiom(individual=OWLNamedIndividual(IRI('http://example.com/father#', 'stefan')),class_expression=OWLClass(IRI('http://example.com/father#', 'male')),annotations=[])
# OWLObjectPropertyAssertionAxiom(subject=OWLNamedIndividual(IRI('http://example.com/father#', 'markus')),property_=OWLObjectProperty(IRI('http://example.com/father#', 'hasChild')),object_=OWLNamedIndividual(IRI('http://example.com/father#', 'anna')),annotations=[])
# OWLObjectPropertyAssertionAxiom(subject=OWLNamedIndividual(IRI('http://example.com/father#', 'martin')),property_=OWLObjectProperty(IRI('http://example.com/father#', 'hasChild')),object_=OWLNamedIndividual(IRI('http://example.com/father#', 'heinz')),annotations=[])
# OWLObjectPropertyAssertionAxiom(subject=OWLNamedIndividual(IRI('http://example.com/father#', 'stefan')),property_=OWLObjectProperty(IRI('http://example.com/father#', 'hasChild')),object_=OWLNamedIndividual(IRI('http://example.com/father#', 'markus')),annotations=[])
# OWLObjectPropertyAssertionAxiom(subject=OWLNamedIndividual(IRI('http://example.com/father#', 'anna')),property_=OWLObjectProperty(IRI('http://example.com/father#', 'hasChild')),object_=OWLNamedIndividual(IRI('http://example.com/father#', 'heinz')),annotations=[])

OWL Knowledge Engineering

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from owlapy.class_expression import OWLClass, OWLObjectIntersectionOf, OWLObjectSomeValuesFrom
from owlapy.owl_property import OWLObjectProperty
from owlapy import owl_expression_to_sparql, owl_expression_to_dl
from owlapy.owl_axiom import OWLDeclarationAxiom, OWLClassAssertionAxiom
from owlapy.owl_individual import OWLNamedIndividual
from owlapy.util_owl_static_funcs import create_ontology
# Using owl classes to create a complex class expression
male = OWLClass("http://example.com/society#male")
hasChild = OWLObjectProperty("http://example.com/society#hasChild")
hasChild_male = OWLObjectSomeValuesFrom(hasChild, male)
teacher = OWLClass("http://example.com/society#teacher")
teacher_that_hasChild_male = OWLObjectIntersectionOf([hasChild_male, teacher])

# You can render and print owl class expressions in Description Logics syntax or convert it to SPARQL for example.
print(owl_expression_to_dl(teacher_that_hasChild_male)) # (∃ hasChild.male) ⊓ teacher
print(owl_expression_to_sparql(teacher_that_hasChild_male)) #  SELECT DISTINCT ?x WHERE {  ?x <http://example.com/society#hasChild> ?s_1 . ?s_1 a <http://example.com/society#male> . ?x a <http://example.com/society#teacher> .  } }

# Create an ontology
ontology = create_ontology("file:/example_ontology.owl",with_owlapi=False)
john = OWLNamedIndividual("http://example.com/society#john")
male_declaration_axiom = OWLDeclarationAxiom(male)
hasChild_declaration_axiom = OWLDeclarationAxiom(hasChild)
john_declaration_axiom = OWLDeclarationAxiom(john)
john_a_male_assertion_axiom = OWLClassAssertionAxiom(john, male)
ontology.add_axiom([male_declaration_axiom, hasChild_declaration_axiom, john_declaration_axiom, john_a_male_assertion_axiom])
ontology.save(inplace=True)

Every OWL object that can be used to classify individuals, is considered a class expression and inherits from OWLClassExpression class. In the above examples we have introduced 3 types of class expressions:

• OWLClass,
• OWLObjectSomeValuesFrom
• OWLObjectIntersectionOf.

Like we showed in this example, you can create all kinds of class expressions using the OWL objects in owlapy api.

Logical Inference

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from owlapy.owl_reasoner import SyncReasoner
from owlapy.static_funcs import stopJVM
from owlapy.owl_ontology import Ontology

ontology_path = "KGs/Family/family-benchmark_rich_background.owl"
# Available OWL Reasoners: 'HermiT', 'Pellet', 'JFact', 'Openllet', 'ELK', 'Structural'
sync_reasoner = SyncReasoner(ontology = ontology_path, reasoner="Pellet")
onto = Ontology(ontology_path)
# Iterate over defined owl Classes in the signature
for i in onto.classes_in_signature():
    # Performing type inference with Pellet
    instances=sync_reasoner.instances(i,direct=False)
    print(f"Class:{i}\t Num instances:{len(instances)}")
stopJVM()

Ontology Enrichment

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An Ontology can be enriched by inferring many different axioms.

from owlapy.owl_reasoner import SyncReasoner
from owlapy.static_funcs import stopJVM

sync_reasoner = SyncReasoner(ontology="KGs/Family/family-benchmark_rich_background.owl", reasoner="Pellet")
# Infer missing class assertions
sync_reasoner.infer_axioms_and_save(output_path="KGs/Family/inferred_family-benchmark_rich_background.ttl",
                       output_format="ttl",
                       inference_types=[
                           "InferredClassAssertionAxiomGenerator",
                           "InferredEquivalentClassAxiomGenerator",
                           "InferredDisjointClassesAxiomGenerator",
                                        "InferredSubClassAxiomGenerator",
                                        "InferredInverseObjectPropertiesAxiomGenerator",
                                        "InferredEquivalentClassAxiomGenerator"])
stopJVM()

Check also the examples and tests folders.

Sklearn to OWL Ontology

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from owlapy.owl_ontology import SyncOntology
from owlapy.util_owl_static_funcs import csv_to_rdf_kg
import pandas as pd
from sklearn.datasets import load_iris
data = load_iris()
df = pd.DataFrame(data.data, columns=data.feature_names)
df.to_csv("iris_dataset.csv", index=False)
path_kg = "iris_kg.owl"
# Construct an RDF Knowledge Graph from a CSV file
csv_to_rdf_kg(path_csv="iris_dataset.csv", path_kg=path_kg, namespace="http://owlapy.com/iris")
onto = SyncOntology(path_kg)
print(len(onto.get_abox_axioms()))

Create Justifications

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from owlapy.owl_individual import OWLNamedIndividual
from owlapy.owl_reasoner import SyncReasoner
from owlapy.owl_ontology import SyncOntology
from owlapy import manchester_to_owl_expression

individual = OWLNamedIndividual("http://www.benchmark.org/family#F1F2")
manchester_expr_str = "hasChild some Female"

ontology = SyncOntology("../KGs/Family/family-benchmark_rich_background.owl")
reasoner = SyncReasoner(ontology, reasoner="Pellet")
target_class = manchester_to_owl_expression(manchester_expr_str, "http://www.benchmark.org/family#")
justifications = reasoner.create_justifications({individual}, target_class, save=True)
[print(justification) for justification in justifications]

Ontology Generation

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from owlapy.ontogen.data_extraction import GraphExtractor
from owlapy.owl_ontology import SyncOntology

text_example = """J.P. Morgan & Co. is an American financial institution specialized in investment banking, 
asset management and private banking founded by financier J. P. Morgan in 1871. Through a series of mergers and 
acquisitions, the company is now a subsidiary of JPMorgan Chase, the largest banking institution in the world. 
The company has been historically referred to as the "House of Morgan" or simply Morgan."""

# Extract a graph from text using an LLM
ontogen = GraphExtractor(model="<ENTER_MODELS_NAME> (e.g. 'Qwen/Qwen3-32B-AWQ')",api_key="<ENTER_YOUR_KEY>", api_base="<ENTER_YOUR_API_BASE_URL>",
                         temperature=0.1, seed=42, enable_logging=True)
ontogen.forward(text=text_example, generate_types = True, extract_spl_triples=True)

# Open the generated ontology and print axioms
onto = SyncOntology(path="generated_ontology.owl")
[print(ax) for ax in onto.get_abox_axioms()]
[print(ax) for ax in onto.get_tbox_axioms()]

Reasoners Runtime Benchmark

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To generate and print the following tables:

wget https://files.dice-research.org/projects/Ontolearn/KGs.zip -O ./KGs.zip && unzip KGs.zip

cd examples && python runtime_benchmark_results.py --pretty_print

Instance retrieval runtime (in seconds) of each reasoner for different class expressions in Family dataset:

Class Expressions	StructuralReasoner	HermiT	Pellet	Openllet	JFact	ELK	Structural
Person	0.0007	0.0251	0.0238	0.0128	0.1726	0.1526	0.0748
(¬Parent)	0.0005	0.3532	0.004	0.0032	0.0046	0.0205	0.0015
∀ hasParent.Father	0.0004	0.3108	0.0043	0.0035	0.006	0.0038	0.001
∃ hasSibling.Daughter	0.0003	0.3176	0.005	0.0057	0.011	0.0103	0.0008
∃ hasChild.(¬Parent)	0.0005	0.3335	0.004	0.0042	0.0102	0.0065	0.0013
≥ 1 married.Male	0.0003	0.3129	0.1711	0.143	0.0101	0.0035	0.001
≤ 3 hasChild.Person	0.0006	0.3114	0.003	0.0038	0.0044	0.0032	0.0008
Brother ⊓ Parent	0.0003	0.1445	0.0039	0.0032	0.0028	0.0112	0.0007
Mother ⊔ Father	0.0003	0.0502	0.0063	0.008	0.0071	0.0167	0.0005
∃ hasParent.{F9M170 ⊔ F9M147 ⊔ F7M128}	0.0006	0.3107	0.0152	0.033	0.0089	0.0063	0.0017

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Instance retrieval runtime (in seconds) of each reasoner for different class expressions in Carcinogenesis dataset:

Class Expressions	StructuralReasoner	HermiT	Pellet	Openllet	JFact	ELK	Structural
Sulfur	0.0012	0.5098	0.3415	0.3124	30.9185	1.0194	0.0821
Structure	0.0004	0.0542	0.067	0.0677	0.0571	0.1922	0.0527
¬Structure	0.0004	225.7262	0.2838	0.3073	0.3142	0.0465	0.0027
∀ hasAtom.Atom	0.0004	0.2862	0.3253	0.3112	0.3378	0.0063	0.0010
∃ hasStructure.Amino	0.0005	20.5614	0.0586	0.1081	0.2986	0.0343	0.0011
≥ 2 inBond.⊤	0.0003	593.4231	0.4509	0.4633	7.8003	0.0055	0.0007
≤ 3 hasAtom.⊤	0.0002	21.5695	0.3497	0.3092	0.3407	0.0035	0.0005
Ring_size_4 ⊓ Sulfur	0.0004	2932.3817	0.0281	0.0163	0.0187	0.0232	0.0008
Bond-7 ⊔ Bond-3	0.0003	486.6015	0.0838	0.0654	0.05	0.1009	0.0007
∃ hasBond.{bond1838 ⊔ bond1879 ⊔ bond1834}	0.0006	24.3014	1.6182	1.2811	0.3255	0.0391	0.0012
∃ isMutagenic.{True}	0.0233	26.6729	32.31	28.9644	0.1972	0.012	0.0006
∃ charge.xsd:double[> 0.1]	0.0008	626.9762	752.119	750.1382	0.2102	0.006	0.0008
Compound ⊓ ∃ isMutagenic.{True}	0.0009	21.8479	28.4732	29.7676	0.1918	0.0189	0.0007
Carbon ⊓ ∃ charge.xsd:double[> 0.1]	0.0005	245.4081	734.3972	747.7481	0.0998	0.0031	0.0007

How to cite

Currently, we are working on our manuscript describing our framework.