OWLAPY: A Pythonic Framework for OWL Ontology Engineering

2025-11-11

AI Summary

To address the weak Python ecosystem for OWL 2 ontology engineering—characterized by poor format interoperability and complex reasoning integration—this paper proposes and implements OntoPy, a lightweight, modular Python framework for ontology development. OntoPy supports declarative ontology construction, dynamic modification, and serialization to multiple formats (RDF/XML, Turtle, JSON-LD). It introduces a unified interface enabling seamless coordination between native Python reasoners (e.g., OWL-RL) and external Java-based reasoners (e.g., HermiT, Pellet). A key innovation is bidirectional translation among description logic (DL) expressions, Manchester Syntax, and SPARQL patterns for OWL class definitions. Additionally, OntoPy provides an LLM-extensible workflow plugin mechanism. The framework is open-source (GitHub/PyPI) and has garnered over 50,000 downloads, significantly improving ontology modeling efficiency and cross-stack interoperability.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

In this paper, we introduce OWLAPY, a comprehensive Python framework for OWL ontology engineering. OWLAPY streamlines the creation, modification, and serialization of OWL 2 ontologies. It uniquely integrates native Python-based reasoners with support for external Java reasoners, offering flexibility for users. OWLAPY facilitates multiple implementations of core ontology components and provides robust conversion capabilities between OWL class expressions and formats such as Description Logics, Manchester Syntax, and SPARQL. It also allows users to define custom workflows to leverage large language models (LLMs) in ontology generation from natural language text. OWLAPY serves as a well-tested software framework for users seeking a flexible Python library for advanced ontology engineering, including those transitioning from Java-based environments. The project is publicly available on GitHub at https://github.com/dice-group/owlapy and on the Python Package Index (PyPI) at https://pypi.org/project/owlapy/ , with over 50,000 downloads at the time of writing.

Problem

• Streamlining OWL 2 ontology creation and modification processes
• Integrating Python and Java reasoners for flexible ontology engineering
• Enabling ontology generation from natural language using LLMs

Innovation

• Python framework for OWL ontology engineering
• Integrates Python and Java reasoners for flexibility
• Enables custom LLM workflows for text-to-ontology generation