SciKnowOrg/ontolearner-materials_science_and_engineering

pip install ontolearner

from ontolearner import BattINFO

ontology = BattINFO()

# Load an ontology.
ontology.load()

# Load (or extract) LLMs4OL Paradigm tasks datasets
data = ontology.extract()

# Import core modules from the OntoLearner library
from ontolearner import BattINFO, LearnerPipeline, train_test_split

# Load the BattINFO ontology, which contains concepts related to wines, their properties, and categories
ontology = BattINFO()
ontology.load()  # Load entities, types, and structured term annotations from the ontology
ontological_data = ontology.extract()

# Split instances into train and test sets
train_data, test_data = train_test_split(ontological_data, test_size=0.2, random_state=42)

# Initialize a multi-component learning pipeline (retriever + LLM)
# This configuration enables a Retrieval-Augmented Generation (RAG) setup
pipeline = LearnerPipeline(
    retriever_id='sentence-transformers/all-MiniLM-L6-v2',      # Dense retriever model for nearest neighbor search
    llm_id='Qwen/Qwen2.5-0.5B-Instruct',                        # Lightweight instruction-tuned LLM for reasoning
    hf_token='...',                                             # Hugging Face token for accessing gated models
    batch_size=32,                                              # Batch size for training/prediction if supported
    top_k=5                                                     # Number of top retrievals to include in RAG prompting
)

# Run the pipeline: training, prediction, and evaluation in one call
outputs = pipeline(
    train_data=train_data,
    test_data=test_data,
    evaluate=True,              # Compute metrics like precision, recall, and F1
    task='term-typing'          # Specifies the task
                                # Other options: "taxonomy-discovery" or "non-taxonomy-discovery"
)

# Print final evaluation metrics
print("Metrics:", outputs['metrics'])

# Print the total time taken for the full pipeline execution
print("Elapsed time:", outputs['elapsed_time'])

# Print all outputs (including predictions)
print(outputs)

@inproceedings{babaei2023llms4ol,
  title={LLMs4OL: Large language models for ontology learning},
  author={Babaei Giglou, Hamed and D’Souza, Jennifer and Auer, S{"o}ren},
  booktitle={International Semantic Web Conference},
  pages={408--427},
  year={2023},
  organization={Springer}
}