README.md

---
license: mit
language:
- en
tags:
- OntoLearner
- ontology-learning
- education
pretty_name: Education
---
<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;">Education 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 education domain encompasses ontologies that systematically represent and organize knowledge related to learning content, educational programs, competencies, and teaching resources. This domain plays a critical role in facilitating semantic interoperability and enhancing the precision of information retrieval and management within educational contexts. By providing a structured framework for the representation of educational concepts and relationships, it supports the development of intelligent systems that can effectively process and utilize educational data.

## Ontologies
| Ontology ID | Full Name | Classes | Properties | Last Updated |
|-------------|-----------|---------|------------|--------------|
| BIBFRAME | Bibliographic Framework Ontology (BIBFRAME) | 212 | 215 | 2022-10-03|
| Common | Common Ontology (Common) | 6 | 15 | None|
| DoCO | Document Components Ontology (DoCO) | 137 | 7 | 2015-07-03|

## 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 BIBFRAME

ontology = BIBFRAME()

# 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 BIBFRAME, LearnerPipeline, train_test_split

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