SciKnowOrg/ontolearner-food_and_beverage

Food And Beverage Domain Ontologies

Overview

The food_and_beverage domain encompasses the structured representation and categorization of knowledge related to food items, beverages, their ingredients, and culinary processes. This domain plays a critical role in facilitating semantic interoperability, data integration, and advanced information retrieval across diverse applications, including gastronomy, nutrition, and food science. By providing a formalized framework, it enables precise communication and analysis within and across disciplines, enhancing both academic research and practical applications in the food and beverage industry.

Ontologies

Ontology ID	Full Name	Classes	Properties	Last Updated
Wine	Wine Ontology (Wine)	101	13	None

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:

pip install ontolearner

How to load an ontology or LLM4OL Paradigm tasks datasets?

from ontolearner import Wine

ontology = Wine()

# 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?

from ontolearner import Wine, LearnerPipeline, train_test_split

ontology = Wine()
ontology.load() 
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 documentation, see the

Citation

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

@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}
}