SciKnowOrg/ontolearner-library_and_cultural_heritage

Library And Cultural Heritage Domain Ontologies

Overview

The library_and_cultural_heritage domain encompasses ontologies that facilitate the systematic organization, cataloging, and preservation of cultural and scholarly resources. This domain plays a critical role in knowledge representation by ensuring consistent metadata standards, enabling interoperability across diverse information systems, and supporting the discovery and retrieval of cultural assets. It is integral to maintaining the integrity and accessibility of cultural heritage and scholarly communication in both digital and physical environments.

Ontologies

Ontology ID	Full Name	Classes	Properties	Last Updated
GND	Gemeinsame Normdatei (GND)	247	236	2024-08-26

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 GND

ontology = GND()

# 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 GND, LearnerPipeline, train_test_split

ontology = GND()
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}
}