---
license: mit
language:
- en
tags:
- OntoLearner
- ontology-learning
- events
pretty_name: Events
---
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  <h1 style="text-align: center; margin-top: 1em;">Events Domain Ontologies</h1>
</div>

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## Overview
The events domain encompasses the structured representation and semantic modeling of occurrences in time, including their temporal, spatial, and contextual attributes. This domain is pivotal in knowledge representation as it facilitates the interoperability and integration of event-related data across diverse systems, enabling precise scheduling, planning, and historical analysis. By providing a framework for understanding and linking events, this domain supports advanced applications in areas such as artificial intelligence, information retrieval, and decision support systems.

## Ontologies
| Ontology ID | Full Name | Classes | Properties | Last Updated |
|-------------|-----------|---------|------------|--------------|
| Conference | Conference Ontology (Conference) | 42 | 52 | 2016/04/30|
| iCalendar | iCalendar Vocabulary (iCalendar) | 54 | 49 | 2004/04/07|
| LODE | Linking Open Descriptions of Events (LODE) | 1 | 7 | 2020-10-31|

## Dataset Files
Each ontology directory contains the following files:
1. `<ontology_id>.<format>` - The original ontology file
2. `term_typings.json` - 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:

```python
from ontolearner.ontology import Wine
from ontolearner.utils.train_test_split import train_test_split
from ontolearner.learner_pipeline import LearnerPipeline

ontology = Wine()
ontology.load()  # Automatically downloads from Hugging Face

# Extract the dataset
data = ontology.extract()

# Split into train and test sets
train_data, test_data = train_test_split(data, test_size=0.2)

# Create a learning pipeline (for RAG-based learning)
pipeline = LearnerPipeline(
    task="term-typing",  # Other options: "taxonomy-discovery" or "non-taxonomy-discovery"
    retriever_id="sentence-transformers/all-MiniLM-L6-v2", 
    llm_id="mistralai/Mistral-7B-Instruct-v0.1",
    hf_token="your_huggingface_token"  # Only needed for gated models
)

# Train and evaluate
results, metrics = pipeline.fit_predict_evaluate(
    train_data=train_data,
    test_data=test_data,
    top_k=3,
    test_limit=10
)
```

For more detailed examples, see the [OntoLearner documentation](https://ontolearner.readthedocs.io/).