SciKnowOrg/ontolearner-industry

Industry Domain Ontologies

Overview

The "industry" domain encompasses ontologies that systematically represent and model the complex structures, processes, and interactions within industrial settings, including manufacturing systems, smart buildings, and equipment. This domain is pivotal in advancing knowledge representation by enabling the integration, interoperability, and automation of industrial processes, thereby facilitating improved efficiency, innovation, and decision-making. Through precise semantic frameworks, it supports the digital transformation and intelligent management of industrial operations.

Ontology ID	Full Name	Classes	Properties	Individuals
AUTO	Automotive Ontology (AUTO)	1372	336	58
DBO	Digital Buildings Ontology (DBO)	3032	7	35
PTO	Product Types Ontology (PTO)	1002	0	3002
IOF	Industrial Ontology Foundry (IOF)	212	51	0
TUBES	TUBES System Ontology (TUBES)	52	101	0
DOAP	The Description of a Project vocabulary (DOAP)	14	0	0
PKO	Provenance Knowledge Ontology (PKO)	38	93	8

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},
}