OntoPyLPG

This project provides a Python-based framework to map OWL ontologies to a labeled property graph (LPG) in Neo4j, infer new knowledge directly on the graph using a custom reasoner, and explore the ontology through a Streamlit-based frontend.

Motivation

OWL ontologies are expressive but RDF-based triple stores can be limited when it comes to graph analytics. This project explores how OWL ontologies can be persisted and reasoned over in a graph database like Neo4j, leveraging its native Cypher querying and graph pattern matching.

Project Structure

• inputs/
  Folder where OWL ontology input files are placed for processing.

• outputs/
  Folder to store outputs like inferred ontology files or exported graph data.

• tests/
  Contains test scripts (e.g., pytest-based unit tests) to validate functionality of the project modules.

• .env
  Stores environment variables like Neo4j credentials.

• .gitignore
  Specifies intentionally untracked files to ignore in the Git repository.

• README.md
  Project documentation and usage instructions.

• requirements.txt
  List of Python package dependencies.

• frontend.py
  Streamlit-based GUI for user interaction (upload, connect, query).

• main.py
  CLI entry point for running the full processing pipeline.

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ontopylpg/

Main Python package containing all ontology-to-graph functionality.

• Connector.py
  Manages the connection to the Neo4j database.

• GraphMetaData.py
  Extracts and stores ontology-level metadata.

• GraphReasoner.py
  Core reasoning logic for subclass, object property, inverse property, etc.

• Mapper.py
  Converts OWL constructs into Neo4j property graph elements.

• OWLHelper.py
  Helper functions for OWL term resolution and formatting.

• OWLReadyReasoner.py
  Integrates Owlready2's built-in reasoner (if needed).

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ontopylpg/equivalent_reasoner/

Submodule for handling equivalence-specific reasoning.

• EquivalenceReasoner.py
  Abstract class to implement equivalent reasoning functions.

• EquivalenceReasoner1.py
  Concrete implementation of equivalence reasoning.

Features

• Upload and parse OWL ontologies using Owlready2.
• Map OWL constructs to Neo4j's LPG model using py2neo.
• Perform custom reasoning directly on the Neo4j graph
• Interactive frontend using Streamlit.
• Modular and extensible codebase.

Setup

1. Install dependencies:

   pip install -r requirements.txt