SciKnowOrg/ontolearner-agriculture

Agriculture Domain Ontologies

Overview

The agriculture domain encompasses the structured representation and systematic study of farming systems, crop cultivation, livestock management, and food production processes. It focuses on the development and utilization of comprehensive agricultural vocabularies and taxonomies to facilitate precise knowledge representation and interoperability across diverse agricultural practices and technologies. This domain is pivotal in advancing sustainable agricultural practices, enhancing food security, and supporting decision-making through the integration of scientific, economic, and environmental data.

Ontology ID	Full Name	Classes	Properties	Individuals
AGROVOC	AGROVOC Multilingual Thesaurus (AGROVOC)	35	209	1234769
FoodOn	Food Ontology (FoodON)	47261	197	16
AgrO	Agronomy Ontology (AgrO)	5778	209	326
PO	Plant Ontology (PO)	1874	13	0
ATOL	Animal Trait Ontology for Livestock (ATOL)	2352	0	0

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 import LearnerPipeline, AutoLearnerLLM, Wine, train_test_split

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

# 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.

Citation

If you use these ontologies in your research, please cite:

@software{babaei_giglou_2025,
  author       = {Babaei Giglou, Hamed and D'Souza, Jennifer and Aioanei, Andrei and Mihindukulasooriya, Nandana and Auer, Sören},
  title        = {OntoLearner: A Modular Python Library for Ontology Learning with LLMs},
  month        = may,
  year         = 2025,
  publisher    = {Zenodo},
  version      = {v1.0.1},
  doi          = {10.5281/zenodo.15399783},
  url          = {https://doi.org/10.5281/zenodo.15399783},
}