SciKnowOrg/ontolearner-general_knowledge

General Knowledge Domain Ontologies

Overview

The general knowledge domain encompasses broad-scope ontologies and upper vocabularies designed for cross-disciplinary semantic modeling and knowledge representation. This domain is pivotal in facilitating interoperability and data integration across diverse fields by providing a foundational framework for organizing and linking information. Its significance lies in enabling the seamless exchange and understanding of knowledge across varied contexts, thereby supporting advanced data analysis, information retrieval, and decision-making processes.

Ontologies

Ontology ID	Full Name	Classes	Properties	Last Updated
CCO	Common Core Ontologies (CCO)	1539	277	2024-11-06
DBpedia	DBpedia Ontology (DBpedia)	790	3029	2008-11-17
DublinCore	Dublin Core Vocabulary (DublinCore)	11	0	February 17, 2017
EDAM	The ontology of data analysis and management (EDAM)	3513	12	24.09.2024
GIST	GIST Upper Ontology (GIST)	199	113	2024-Feb-27
IAO	Information Artifact Ontology (IAO)	292	57	2022-11-07
PROV	PROV Ontology (PROV-O)	39	50	2013-04-30
RO	Relation Ontology (RO)	88	673	2024-04-24
SchemaOrg	Schema.org Ontology (SchemaOrg)	3881	1485	2024-11-22
UMBEL	Upper Mapping and Binding Exchange Layer Vocabulary (UMBEL)	99	42	May 10, 2016
YAGO	YAGO Ontology (YAGO)	N/A	N/A	April, 2024

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 CCO

ontology = CCO()

# 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?

# Import core modules from the OntoLearner library
from ontolearner import CCO, LearnerPipeline, train_test_split

# Load the CCO ontology, which contains concepts related to wines, their properties, and categories
ontology = CCO()
ontology.load()  # Load entities, types, and structured term annotations from the ontology
data = ontology.extract()

# Split into train and test sets
train_data, test_data = train_test_split(data, test_size=0.2, random_state=42)

# Initialize a multi-component learning pipeline (retriever + LLM)
# This configuration enables a Retrieval-Augmented Generation (RAG) setup
pipeline = LearnerPipeline(
    retriever_id='sentence-transformers/all-MiniLM-L6-v2',      # Dense retriever model for nearest neighbor search
    llm_id='Qwen/Qwen2.5-0.5B-Instruct',                        # Lightweight instruction-tuned LLM for reasoning
    hf_token='...',                                             # Hugging Face token for accessing gated models
    batch_size=32,                                              # Batch size for training/prediction if supported
    top_k=5                                                     # Number of top retrievals to include in RAG prompting
)

# Run the pipeline: training, prediction, and evaluation in one call
outputs = pipeline(
    train_data=train_data,
    test_data=test_data,
    evaluate=True,              # Compute metrics like precision, recall, and F1
    task='term-typing'          # Specifies the task
                                # Other options: "taxonomy-discovery" or "non-taxonomy-discovery"
)

# Print final evaluation metrics
print("Metrics:", outputs['metrics'])

# Print the total time taken for the full pipeline execution
print("Elapsed time:", outputs['elapsed_time'])

# Print all outputs (including predictions)
print(outputs)

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}
}