SciKnowOrg/ontolearner-upper_ontology

Upper Ontology Domain Ontologies

Overview

Upper ontology, also known as a foundational ontology, encompasses a set of highly abstract, domain-independent concepts that serve as the building blocks for more specialized ontologies. These ontologies provide a structured framework for representing fundamental entities such as objects, processes, and relations, facilitating interoperability and semantic integration across diverse domains. By establishing a common vocabulary and set of principles, upper ontologies play a crucial role in enhancing the consistency and coherence of knowledge representation systems.

Ontologies

Ontology ID	Full Name	Classes	Properties	Last Updated
BFO	Basic Formal Ontology (BFO)	84	40	2020
DOLCE	Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE)	44	70	None
FAIR	FAIR Vocabulary (FAIR)	7	1	None
GFO	General Formal Ontology (GFO)	94	67	2024-11-18
SIO	Semanticscience Integrated Ontology (SIO)	1726	212	03/25/2024
SUMO	Suggested Upper Merged Ontology (SUMO)	4525	587	2025-02-17

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 BFO

ontology = BFO()

# 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 BFO, LearnerPipeline, train_test_split

ontology = BFO()
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}
}