SciKnowOrg/ontolearner-ecology_and_environment

Ecology And Environment Domain Ontologies

Overview

The ecology and environment domain encompasses the structured representation of knowledge pertaining to ecological systems, diverse environmental contexts, biomes, and the principles of sustainability science. This domain is pivotal in facilitating the integration, sharing, and analysis of complex environmental data, thereby advancing our understanding of ecological interactions and promoting informed decision-making for sustainable development. By providing a formalized framework, it supports interdisciplinary research and policy-making aimed at addressing global environmental challenges.

Ontologies

Ontology ID	Full Name	Classes	Properties	Last Updated
ENVO	Environment Ontology (ENVO)	9309	208	2024-07-01
OEO	The Open Energy Ontology (OEO)	0	0	03/2025
SWEET	Semantic Web for Earth and Environment Technology Ontology (SWEET)	10240	358	July 14, 2022

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 ENVO

ontology = ENVO()

# 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 ENVO, LearnerPipeline, train_test_split

ontology = ENVO()
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}
}