SciKnowOrg/ontolearner-biology_and_life_sciences

Biology And Life Sciences Domain Ontologies

Overview

The biology and life sciences domain encompasses the structured representation and categorization of knowledge related to biological entities, processes, and systems, ranging from molecular and cellular levels to complex organisms and ecosystems. This domain plays a critical role in facilitating data interoperability, integration, and retrieval across diverse biological disciplines, thereby advancing research and discovery. By providing a formalized framework for understanding biological concepts and their interrelations, it supports the development of computational models and enhances the precision of scientific communication.

Ontologies

Ontology ID	Full Name	Classes	Properties	Last Updated
BioPAX	Biological Pathways Exchange (BioPAX)	92	96	16 April 2015
EFO	Experimental Factor Ontology (EFO)	88311	87	2025-02-17
GO	Gene Ontology (GO)	62046	9	2024-11-03
LIFO	Life Ontology (LifO)	239	98	March 11, 2018
MarineTLO	Marine Taxonomy and Life Ontology (MarineTLO)	104	94	2017-01-05
MGED	MGED Ontology (MGED)	233	121	Feb. 9, 2007
MO	Microscopy Ontology (MO)	1066	70	None
NPO	NanoParticle Ontology (NPO)	2464	87	2013-05-31
PATO	Phenotype and Trait Ontology (PATO)	13544	252	2025-02-01

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 BioPAX

ontology = BioPAX()

# 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 BioPAX, LearnerPipeline, train_test_split

ontology = BioPAX()
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}
}