OWLAPY

The Python Framework for Modern Ontology Engineering and Knowledge Graph Development

OWLAPY brings the power of OWL ontologies to Python's rich data science and AI ecosystem. Built for researchers, data scientists, and knowledge engineers who want to leverage semantic web technologies without leaving Python.

🚀 Why OWLAPY?

Native Python Integration

Pythonic API: Work with ontologies using intuitive Python syntax, not verbose Java-style code
Perfect for Machine Learning: Atomic structure makes it easy to integrate with ML pipelines and data science workflows
LLM-Powered Knowledge Extraction: Fully automatic, scalable, agentic ontology (KG) generation from unstructured text using LLMs

Production-Ready Reasoning

Python-native Reasoners: Structural Reasoner and Embedding-based Reasoner
Optimized Performance: Benchmarked across complex ontologies (see our performance data)
Synchronization with Java Reasoners: HermiT, Pellet, JFact, Openllet, ELK, and Structural

Cutting-Edge Features

🆕 Automated Ontology Generation: Extract knowledge graphs from text using LLMs with zero manual annotation through our agentic pipeline AGen-KG (scalable to large documents).
Class Expression Simplification: Automatically simplify complex logical expressions
Syntax Conversion: Convert between Manchester, DL, and SPARQL syntaxes effortlessly

Other Functionalities

Synchronization with OWLAPI: Access to OWLAPI's features via a Pythonic interface
Support for SWRL Rules: Create and manipulate SWRL rules in Python

Research-Backed

OWLAPY is actively developed by the DICE research group of Paderborn University. Our framework powers cutting-edge research in knowledge graph engineering, concept learning, and semantic reasoning.

Documentation | DeepWiki Assistant

🎯 What Can You Build?

Knowledge Graph Pipelines: Transform raw data into rich, queryable knowledge graphs
AI-Enhanced Ontologies: Leverage LLMs to extract structured knowledge from documents
Semantic Search Systems: Build intelligent search with logical reasoning
Data Integration: Unify heterogeneous data sources with formal semantics
Explainable AI: Add logical reasoning and justifications to ML pipelines

Installation

⚡ Quick Start using PyPI

pip3 install owlapy

🌱 Installation from Source

git clone https://github.com/dice-group/owlapy && cd owlapy

conda create -n temp_owlapy python=3.10.13 --no-default-packages && conda activate temp_owlapy && pip install -e '.[dev]'

Extra Files (optional)

# Download RDF knowledge graphs
wget https://files.dice-research.org/projects/Ontolearn/KGs.zip -O ./KGs.zip && unzip KGs.zip

# Test with pytest
PYTHONPATH=. pytest

📋 Examples

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:

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

Ontology Generation

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Our latest feature employees a combination of state-of-the-art approaches to extract knowledge graphs from unstructured text using Large Language Models (LLMs). The algorithm consist of an agentic pipeline called AGen-KG (stands for agent-generated KG) which can scale to large documents through chunking and merging strategies.

from owlapy.agen_kg import AGenKG
from owlapy.owl_ontology import SyncOntology


filep = "examples/doctors_notes.txt" # make sure the path is pointing correctly

# This example is set up to use GitHub's Models by providing your 
# GitHub Personal Access Token (PAT) because it's for free (subject to change),
# but of course, you can use any model & API base of your choice.
agent = AGenKG(model="gpt-4o", api_key="<YOUR_GITHUB_PAT>",
             api_base="https://models.github.ai/inference",
             temperature=0.1, seed=42, max_tokens=6000, enable_logging=True)
agent.generate_ontology(text=filep,
                        ontology_type="domain",
                        query="I want the resulting graph to represent medical information "
                              "about each patient from the provided doctors' notes.",
                        generate_types=True,
                        extract_spl_triples=True,
                        create_class_hierarchy=False,
                        fact_reassurance=False,
                        save_path="patients.owl")

# === Logs ===
# DomainGraphExtractor: INFO :: Decomposed the query into specific instructions
# UniversalTextLoader: INFO :: Loading text from .txt file: doctors_notes.txt
# UniversalTextLoader: INFO :: Successfully loaded 564 words (4257 characters)
# DomainGraphExtractor: INFO :: Text will be processed in 1 chunks
# DomainGraphExtractor: INFO :: Total chars: 4257, Est. tokens: 1064
# DomainGraphExtractor: INFO :: Detected domain: medicine
# DomainGraphExtractor: INFO :: Generating domain-specific few-shot examples for domain: medicine
# DomainGraphExtractor: INFO :: Generated examples for entity_extraction
# DomainGraphExtractor: INFO :: Generated examples for triples_extraction
# DomainGraphExtractor: INFO :: Generated examples for type_assertion
# DomainGraphExtractor: INFO :: Generated examples for type_generation
# DomainGraphExtractor: INFO :: Generated examples for literal_extraction
# DomainGraphExtractor: INFO :: Generated examples for triples_with_numeric_literals_extraction
# DomainGraphExtractor: INFO :: Cached examples for domain 'medicine' to path_hidden/domain_examples_medicine.json
# DomainGraphExtractor: INFO :: Generated the following entities: ['P001', 'EARLY HYPERTENSION', 'ACE INHIBITORS', 'P002', 'TYPE 2 DIABETES', 'METFORMIN', 'P003', 'SEASONAL FLU', 'ANTIVIRALS', 'P004', 'MECHANICAL BACK PAIN', 'NSAIDS', 'PHYSIOTHERAPY', 'P005', 'MIGRAINES', 'TRIPTANS', 'P006', 'OSTEOARTHRITIS', 'PAIN MANAGEMENT PLAN', 'P007', 'GERD', 'PPIs', 'P008', 'ANXIETY DISORDER', 'CBT', 'SSRIs', 'P009', 'HYPERLIPIDEMIA', 'STATINS', 'P010', 'ASTHMA', 'INHALED BRONCHODILATOR', 'P011', 'COPD', 'BRONCHODILATORS', 'P012', 'IRON DEFICIENCY ANEMIA', 'IRON SUPPLEMENTS', 'P013', 'DEPRESSION', 'ANTIDEPRESSANTS', 'P014', 'IBS', 'DIETARY MODIFICATIONS', 'P015', 'CATARACTS', 'SURGICAL EVALUATION', 'P016', 'HYPOTHYROIDISM', 'LEVOTHYROXINE', 'P017', 'ACUTE PHARYNGITIS', 'ANTIBIOTICS', 'P018', 'RHEUMATOID ARTHRITIS', 'DMARDs', 'P019', 'KIDNEY STONES', 'PAIN RELIEF', 'HYDRATION', 'P020', 'LOW VITAMIN D LEVELS', 'SUPPLEMENTS']
# DomainGraphExtractor: INFO :: Generated the following triples: [('P001', 'HAS', 'EARLY HYPERTENSION'), ('EARLY HYPERTENSION', 'TREATED WITH', 'ACE INHIBITORS'), ('P001', 'PRESCRIBED', 'ACE INHIBITORS'), ('P002', 'HAS', 'TYPE 2 DIABETES'), ('TYPE 2 DIABETES', 'TREATED WITH', 'METFORMIN'), ('P002', 'PRESCRIBED', 'METFORMIN'), ('P003', 'HAS', 'SEASONAL FLU'), ('SEASONAL FLU', 'TREATED WITH', 'ANTIVIRALS'), ('P003', 'PRESCRIBED', 'ANTIVIRALS'), ('P004', 'HAS', 'MECHANICAL BACK PAIN'), ('MECHANICAL BACK PAIN', 'TREATED WITH', 'NSAIDS'), ('P004', 'PRESCRIBED', 'NSAIDS'), ('P004', 'REFERRED TO', 'PHYSIOTHERAPY'), ('P005', 'HAS', 'MIGRAINES'), ('MIGRAINES', 'TREATED WITH', 'TRIPTANS'), ('P005', 'PRESCRIBED', 'TRIPTANS'), ('P006', 'HAS', 'OSTEOARTHRITIS'), ('OSTEOARTHRITIS', 'TREATED WITH', 'PAIN MANAGEMENT PLAN'), ('P007', 'HAS', 'GERD'), ('GERD', 'TREATED WITH', 'PPIs'), ('P007', 'PRESCRIBED', 'PPIs'), ('P008', 'HAS', 'ANXIETY DISORDER'), ('ANXIETY DISORDER', 'TREATED WITH', 'CBT'), ('ANXIETY DISORDER', 'TREATED WITH', 'SSRIs'), ('P008', 'PRESCRIBED', 'SSRIs'), ('P009', 'HAS', 'HYPERLIPIDEMIA'), ('HYPERLIPIDEMIA', 'TREATED WITH', 'STATINS'), ('P009', 'PRESCRIBED', 'STATINS'), ('P010', 'HAS', 'ASTHMA'), ('ASTHMA', 'TREATED WITH', 'INHALED BRONCHODILATOR'), ('P010', 'PRESCRIBED', 'INHALED BRONCHODILATOR'), ('P011', 'HAS', 'COPD'), ('COPD', 'TREATED WITH', 'BRONCHODILATORS'), ('P011', 'PRESCRIBED', 'BRONCHODILATORS'), ('P012', 'HAS', 'IRON DEFICIENCY ANEMIA'), ('IRON DEFICIENCY ANEMIA', 'TREATED WITH', 'IRON SUPPLEMENTS'), ('P012', 'PRESCRIBED', 'IRON SUPPLEMENTS'), ('P013', 'HAS', 'DEPRESSION'), ('DEPRESSION', 'TREATED WITH', 'ANTIDEPRESSANTS'), ('P013', 'PRESCRIBED', 'ANTIDEPRESSANTS'), ('P014', 'HAS', 'IBS'), ('IBS', 'TREATED WITH', 'DIETARY MODIFICATIONS'), ('P015', 'HAS', 'CATARACTS'), ('CATARACTS', 'TREATED WITH', 'SURGICAL EVALUATION'), ('P016', 'HAS', 'HYPOTHYROIDISM'), ('HYPOTHYROIDISM', 'TREATED WITH', 'LEVOTHYROXINE'), ('P016', 'PRESCRIBED', 'LEVOTHYROXINE'), ('P017', 'HAS', 'ACUTE PHARYNGITIS'), ('ACUTE PHARYNGITIS', 'TREATED WITH', 'ANTIBIOTICS'), ('P017', 'PRESCRIBED', 'ANTIBIOTICS'), ('P018', 'HAS', 'RHEUMATOID ARTHRITIS'), ('RHEUMATOID ARTHRITIS', 'TREATED WITH', 'DMARDs'), ('P018', 'PRESCRIBED', 'DMARDs'), ('P019', 'HAS', 'KIDNEY STONES'), ('KIDNEY STONES', 'TREATED WITH', 'PAIN RELIEF'), ('KIDNEY STONES', 'TREATED WITH', 'HYDRATION'), ('P020', 'HAS', 'LOW VITAMIN D LEVELS'), ('LOW VITAMIN D LEVELS', 'TREATED WITH', 'SUPPLEMENTS')]
# DomainGraphExtractor: INFO :: Using summary (3000 chars) for relation clustering
# DomainGraphExtractor: INFO :: Merged 1 duplicate relations
# DomainGraphExtractor: INFO :: After relation clustering: ['TREATED WITH', 'HAS', 'REFERRED TO']
# DomainGraphExtractor: INFO :: Skipped coherence check, using all 58 triples
# DomainGraphExtractor: INFO :: Finished generating types and assigned them to entities as following: [('P001', 'Patient'), ('EARLY HYPERTENSION', 'MedicalCondition'), ('ACE INHIBITORS', 'Medication'), ('P002', 'Patient'), ('TYPE 2 DIABETES', 'MedicalCondition'), ('METFORMIN', 'Medication'), ('P003', 'Patient'), ('SEASONAL FLU', 'MedicalCondition'), ('ANTIVIRALS', 'Medication'), ('P004', 'Patient'), ('MECHANICAL BACK PAIN', 'MedicalCondition'), ('NSAIDS', 'Medication'), ('PHYSIOTHERAPY', 'Procedure'), ('P005', 'Patient'), ('MIGRAINES', 'MedicalCondition'), ('TRIPTANS', 'Medication'), ('P006', 'Patient'), ('OSTEOARTHRITIS', 'MedicalCondition'), ('PAIN MANAGEMENT PLAN', 'Procedure'), ('P007', 'Patient'), ('GERD', 'MedicalCondition'), ('PPIs', 'Medication'), ('P008', 'Patient'), ('ANXIETY DISORDER', 'MedicalCondition'), ('CBT', 'Procedure'), ('SSRIs', 'Medication'), ('P009', 'Patient'), ('HYPERLIPIDEMIA', 'MedicalCondition'), ('STATINS', 'Medication'), ('P010', 'Patient'), ('ASTHMA', 'MedicalCondition'), ('INHALED BRONCHODILATOR', 'Medication'), ('P011', 'Patient'), ('COPD', 'MedicalCondition'), ('BRONCHODILATORS', 'Medication'), ('P012', 'Patient'), ('IRON DEFICIENCY ANEMIA', 'MedicalCondition'), ('IRON SUPPLEMENTS', 'Medication'), ('P013', 'Patient'), ('DEPRESSION', 'MedicalCondition'), ('ANTIDEPRESSANTS', 'Medication'), ('P014', 'Patient'), ('IBS', 'MedicalCondition'), ('DIETARY MODIFICATIONS', 'Procedure'), ('P015', 'Patient'), ('CATARACTS', 'MedicalCondition'), ('SURGICAL EVALUATION', 'Procedure'), ('P016', 'Patient'), ('HYPOTHYROIDISM', 'MedicalCondition'), ('LEVOTHYROXINE', 'Medication'), ('P017', 'Patient'), ('ACUTE PHARYNGITIS', 'MedicalCondition'), ('ANTIBIOTICS', 'Medication'), ('P018', 'Patient'), ('RHEUMATOID ARTHRITIS', 'MedicalCondition'), ('DMARDs', 'Medication'), ('P019', 'Patient'), ('KIDNEY STONES', 'MedicalCondition'), ('PAIN RELIEF', 'Procedure'), ('HYDRATION', 'Procedure'), ('P020', 'Patient'), ('LOW VITAMIN D LEVELS', 'LabResult'), ('SUPPLEMENTS', 'Medication')]
# DomainGraphExtractor: INFO :: Generated the following numeric literals: ['34', '58', '22', '45', '29', '67', '41', '36', '50', '19', '62', '27', '48', '33', '71', '39', '24', '55', '46', '31', '1']
# DomainGraphExtractor: INFO :: Generated the following s-p-l triples: [('P001', 'AGE', '34'), ('P002', 'AGE', '58'), ('P003', 'AGE', '22'), ('P004', 'AGE', '45'), ('P005', 'AGE', '29'), ('P006', 'AGE', '67'), ('P007', 'AGE', '41'), ('P008', 'AGE', '36'), ('P009', 'AGE', '50'), ('P010', 'AGE', '19'), ('P011', 'AGE', '62'), ('P012', 'AGE', '27'), ('P013', 'AGE', '48'), ('P014', 'AGE', '33'), ('P015', 'AGE', '71'), ('P016', 'AGE', '39'), ('P017', 'AGE', '24'), ('P018', 'AGE', '55'), ('P019', 'AGE', '46'), ('P020', 'AGE', '31')]
# Saving patients.owl..


# You can load the generated ontology and work with it as normally
onto = SyncOntology(path="patients.owl")
[print(ax) for ax in onto.get_abox_axioms()]
[print(ax) for ax in onto.get_tbox_axioms()]

You can find this example here.

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"]

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()

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]

Class expression simplification

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Syntactic simplification of class expressions using owlapy.utils.CESimplifier class or directly by using owlapy.utils.simplify_class_expression function.

from owlapy import dl_to_owl_expression, owl_expression_to_dl
from owlapy.utils import simplify_class_expression, get_expression_length

ce_dl = "((((((((((¬Father) ⊓ (¬(∃ hasChild.Grandfather))) ⊓ (¬(∃ hasParent.{F1F2}))) ⊓ (¬(∃ hasSibling.Granddaughter))) \
⊓ Grandson) ⊓ (¬(∃ hasParent.{F8M136}))) ⊔ ((((((¬Father) ⊓ (¬(∃ hasChild.Grandfather))) ⊓ (¬(∃ hasParent.{F1F2}))) ⊓ \
(¬(∃ hasSibling.Granddaughter))) ⊓ (¬Grandson)) ⊓ (∃ married.{F10F179}))) ⊔ (((((¬Father) ⊓ (¬(∃ hasChild.Grandfather))) \
⊓ (¬(∃ hasParent.{F1F2}))) ⊓ (∃ hasSibling.Granddaughter)) ⊓ (¬Grandson))) ⊔ ((¬Father) ⊓ (∃ hasChild.Grandfather))) ⊔ \
(((¬Father) ⊓ (¬(∃ hasChild.Grandfather))) ⊓ (∃ hasParent.{F1F2}))) ⊔ (((Father ⊓ (¬(∃ hasSibling.Grandson))) ⊓ \
(¬(∃ hasChild.{F5M64}))) ⊓ (¬(∃ married.{F2F15})))"
ce_owl = dl_to_owl_expression(ce_dl, "http://www.benchmark.org/family#")
simplified_ce = simplify_class_expression(ce_owl)
print(owl_expression_to_dl(simplified_ce))
print(f"Original CE length: {get_expression_length(ce_owl)} \nSimplified CE length: {get_expression_length(simplified_ce)}")

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

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

Check also the examples and tests directories for more examples.

🌐 Try It Online

Explore OWLAPY through OntoSource - a web-based interface for ontology engineering, no installation required.

📄 How to Cite

If you use OWLAPY in your research, please cite our work:

# OWLAPY
@misc{baci2025owlapypythonicframeworkowl,
      title={OWLAPY: A Pythonic Framework for OWL Ontology Engineering}, 
      author={Alkid Baci and Luke Friedrichs and Caglar Demir and Axel-Cyrille Ngonga Ngomo},
      year={2025},
      eprint={2511.08232},
      archivePrefix={arXiv},
      primaryClass={cs.SE},
      url={https://arxiv.org/abs/2511.08232}, 
}


# EBR
@misc{teyou2025neuralreasoningrobustinstance,
      title={Neural Reasoning for Robust Instance Retrieval in $\mathcal{SHOIQ}$}, 
      author={Louis Mozart Kamdem Teyou and Luke Friedrichs and N'Dah Jean Kouagou and Caglar Demir and Yasir Mahmood and Stefan Heindorf and Axel-Cyrille Ngonga Ngomo},
      year={2025},
      eprint={2510.20457},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2510.20457}, 
}

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OWLAPY

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Native Python Integration

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⚡ Quick Start using PyPI

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OWLAPY

🚀 Why OWLAPY?

Native Python Integration

Production-Ready Reasoning

Cutting-Edge Features

Other Functionalities

Research-Backed

🎯 What Can You Build?

Installation

⚡ Quick Start using PyPI

🌱 Installation from Source

Extra Files (optional)

📋 Examples

Exploring OWL Ontology

OWL Knowledge Engineering

Ontology Generation

OWL Reasoning from Command line

Logical Inference

Ontology Enrichment

Sklearn to OWL Ontology

Create Justifications

Class expression simplification

Reasoners Runtime Benchmark

🌐 Try It Online

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