---
license: mit
language:
- en
tags:
- OntoLearner
- ontology-learning
- medicine
pretty_name: Medicine
---
<div align="center">
  <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;">Medicine Domain Ontologies</h1>
  <a href="https://github.com/sciknoworg/OntoLearner"><img src="https://img.shields.io/badge/GitHub-OntoLearner-blue?logo=github" /></a>
</div>

## Overview
The domain of medicine encompasses the structured representation and systematic organization of clinical knowledge, including the classification and interrelation of diseases, pharmacological agents, therapeutic interventions, and biomedical data. This domain is pivotal for advancing healthcare research, facilitating interoperability among medical information systems, and enhancing decision-making processes through precise and comprehensive knowledge representation. By employing ontologies, this domain ensures a standardized and semantically rich framework that supports the integration and analysis of complex biomedical information.

## Ontologies
| Ontology ID | Full Name | Classes | Properties | Last Updated |
|-------------|-----------|---------|------------|--------------|
| BTO | BRENDA Tissue Ontology (BTO) | 6569 | 10 | 2021-10-26|
| DEB | Devices, Experimental scaffolds and Biomaterials Ontology (DEB) | 601 | 120 | Jun 2, 2021|
| DOID | Human Disease Ontology (DOID) | 15343 | 2 | 2024-12-18|
| ENM | Environmental Noise Measurement Ontology (ENM) | 26142 | 53 | 2025-02-17|
| MFOEM | Mental Functioning Ontology of Emotions - Emotion Module (MFOEM) | 637 | 22 | None|
| NCIt | NCI Thesaurus (NCIt) | N/A | N/A | 2023-10-19|
| OBI | Ontology for Biomedical Investigations (OBI) | 9703 | 94 | 2025-01-09|
| PRotein | Protein Ontology (PRO) | N/A | N/A | 08:08:2024|

## 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 BTO

ontology = BTO()

# 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 BTO, LearnerPipeline, train_test_split

# Load the BTO ontology, which contains concepts related to wines, their properties, and categories
ontology = BTO()
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}
}
```