SciKnowOrg/ontolearner-upper_ontology

Upper Ontology Domain Ontologies

Overview

The upper ontology, also known as a foundational ontology, encompasses a set of highly abstract, domain-independent concepts that serve as the building blocks for more specialized ontologies. These ontologies provide a structured framework for representing fundamental entities such as objects, processes, and relations, facilitating interoperability and semantic integration across diverse domains. By establishing a common vocabulary and set of principles, upper ontologies play a crucial role in enhancing the consistency and coherence of knowledge representation systems.

Ontologies

Ontology ID	Full Name	Classes	Properties	Last Updated
BFO	Basic Formal Ontology (BFO)	84	40	2020
DOLCE	Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE)	44	70	None
FAIR	FAIR Vocabulary (FAIR)	7	1	None
GFO	General Formal Ontology (GFO)	94	67	2024-11-18
SIO	Semanticscience Integrated Ontology (SIO)	1726	212	03/25/2024
SUMO	Suggested Upper Merged Ontology (SUMO)	4525	587	2025-02-17

Dataset Files

Each ontology directory contains the following files:

1. <ontology_id>.<format> - The original ontology file
2. term_typings.json - A Dataset of term-to-type mappings
3. taxonomies.json - Dataset of taxonomic relations
4. non_taxonomic_relations.json - Dataset of non-taxonomic relations
5. <ontology_id>.rst - Documentation describing the ontology

Usage

These datasets are intended for ontology learning research and applications. Here's how to use them with OntoLearner:

First of all, install the OntoLearner library via PiP:

pip install ontolearner

How to load an ontology or LLM4OL Paradigm tasks datasets?

from ontolearner import BFO

ontology = BFO()

# Load an ontology.
ontology.load()  

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

How use the loaded dataset for LLM4OL Paradigm task settings?

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

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

# Split into train and test sets
train_data, test_data = train_test_split(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)

For more detailed documentation, see the

Citation

If you find our work helpful, feel free to give us a cite.

@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}
}