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LLM-Driven DL-Learner Framework

This repository contains supplementary materials and scripts used for evaluating Concept Induction in Description Logic(DL) class expressions through Large Language Models (LLMs).

Contents

📁 Notebooks

LLM_gen_DL-learner_with_resoner.ipynb

This notebook demonstrates:

  • Prompting LLMs (e.g., GPT-4, o3-mini) to generate DL class expressions.
  • Construction of prompts with positive/negative examples and background knowledge.
  • Automatic validation using a Java-based OWL reasoner.
  • Logging of results including correctness and average processing time.

OWL_to_text.ipynb

This utility notebook:

  • Converts OWL ontologies into human-readable natural language text.
  • Is useful for prompt creation or manual inspection of ontology axioms.

Install dependencies

pip install -r requirements.txt

⚙️ Java OWL Reasoner

To validate generated class expressions with a symbolic reasoner, compile and invoke the following Java class:

src/main/java/com/example/MergedReasonerCheck.java

This script uses the OWL API and Pellet reasoner to:

  • Parse class expressions in Manchester OWL syntax.
  • Apply closed-world reasoning using user-defined positive/negative assertions. Closed world reasoning is invoked by calling the Closed World Reasoner class from DL-Learner.
  • Output validation results for class expressions produced by LLMs.

🔍 Note: Closed world reasoning is invoked by calling the Closed World Reasoner class from DL-Learner.

Build Instructions:

To generate the .jar file needed to run the reasoner:

cd supplymentary_materials
mvn clean package

This creates a JAR file typically at:

target/merged-reasoner-check-1.0-SNAPSHOT.jar

Usage Example:

java -jar target/merged-reasoner-check-1.0-SNAPSHOT.jar \
  path/to/ontology.owl \
  http://your-ontology-base-uri# \
- \
  PositiveExample1,PositiveExample2 \
  NegativeExample1,NegativeExample2 <<EOF
  YourClassExpressionHere
EOF

📁 Dataset

The repository also contains folders such as yinyang_examples with:

  • Different ontology configurations for the basicFamily.owl ontology(default, changed gender, changed relations).
  • Positive/negative example sets.
  • DL-Learner outputs.
  • Reasoner validation outputs from different LLMs.

There are two other folders for family-benchmark.owl and trains2.owl ontologies.

📌 Notes

  • Ensure you have Java 11+ and Maven installed to compile the reasoner.
  • LLM outputs are tested with reasoning-based verification for correctness.
  • You may adapt the prompt structure and input formatting based on your ontology structure.