README.md

metadata

license: mit
language:
  - en
tags:
  - OntoLearner
  - ontology-learning
  - events
pretty_name: Events

Events Domain Ontologies

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:

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.