---
license: mit
language:
- en
tags:
- OntoLearner
- ontology-learning
- upper-ontology
pretty_name: Upper Ontology
---
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  <img  src="https://raw.githubusercontent.com/sciknoworg/OntoLearner/main/images/logo.png"  alt="OntoLearner"
    style="display: block; margin: 0 auto; width: 500px; height: auto;">
  <h1 style="text-align: center; margin-top: 1em;">Upper Ontology Domain Ontologies</h1>
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## 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:

```bash
pip install ontolearner
```

**How to load an ontology or LLM4OL Paradigm tasks datasets?**
``` python
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?**
``` python
# 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 [![Documentation](https://img.shields.io/badge/Documentation-ontolearner.readthedocs.io-blue)](https://ontolearner.readthedocs.io)


## Citation

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


```bibtex
@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}
}
```