metadata

language:
  - en
license: cc-by-sa-3.0
license_link: https://creativecommons.org/licenses/by-sa/3.0/
tags:
  - knowledge-graph
  - rdf
  - triples
annotations_creators:
  - crowdsourced
  - expert-generated
source_datasets:
  - wikidata
pretty_name: YAGO 4.5 (EN)
size_categories:
  - 100M<n<1B
task_categories:
  - graph-ml
dataset_info:
  features:
    - name: subject
      dtype: string
    - name: predicate
      dtype: string
    - name: object
      dtype: string
  config_name: default
  splits:
    - name: train
      num_bytes: 42709902295
      num_examples: 249675587
  dataset_size: 42709902295
viewer: false

YAGO 4.5 Dataset (English subset for LLM fine-tuning)

To utilize the YAGO 4.5 (EN) Dataset, users should ensure they have the following prerequisites installed:

Software

Python (Tested with 3.10)
Hugging Face Datasets Library: Required for loading and processing the dataset.
```
pip install datasets
pip install rdflib
```

Hardware

Sufficient Storage: The dataset is approximately 43 GB, ensure you have enough storage space to download and extract the dataset.
Multi-core Processor: For efficient data loading and processing, a multi-core processor is recommended. The more threads the faster you can load the dataset.

Dataset Description

This dataset contains triples filtered from yago-facts.ttl and yago-beyond-wikipedia.ttl in the YAGO 4.5 dataset. The SPARQL query used to filter the triples is in filter.sparql. This represents a subset of the YAGO 4.5 dataset maintaining only English labels.

I remapped some schema.org properties to http://yago-knowledge.org/resource/ which were not present in the schema.org vocabulary. I also removed schema:sameAs and owl:sameAs relations from this dataset, as well as triples with xsd:anyURI object literals, as my goal is to use this dataset for fine-tuning a large language model for knowledge graph completion and I do not want to train the base model to predict these kind of relations.

Overview

YAGO 4.5 is the latest version of the YAGO knowledge base. It is based on Wikidata — the largest public general-purpose knowledge base. YAGO refines the data as follows:

All entity identifiers and property identifiers are human-readable.
The top-level classes come from schema.org — a standard repertoire of classes and properties maintained by Google and others. The lower level classes are a careful selection of the Wikidata taxonomy.
The properties come from schema.org.
YAGO 4.5 contains semantic constraints in the form of SHACL. These constraints keep the data clean, and allow for logical reasoning on YAGO.

Dataset Structure

The dataset is structured as follows:

yago-taxonomy.ttl: Contains the rdfs:subClassOf relations for YAGO and the prefix mappings for the N-Triples.
facts.tar.gz: Compressed file containing chunks of the dataset in N-Triples format, representing the factual knowledge in YAGO.

Features

Each RDF triple in the dataset is represented with the following features:

subject: The subject of the triple, representing the entity.
predicate: The predicate of the triple, representing the relationship between the subject and object.
object: The object of the triple, representing the entity or value linked by the predicate.

Chunks

The dataset is logically divided into multiple chunks, each containing a subset of RDF triples. Users can load specific chunks or the entire dataset based on their requirements.

Usage

Loading the Dataset

The dataset can be loaded using the Hugging Face datasets library as follows:

from datasets import load_dataset

dataset = load_dataset('wikipunk/yago45en', num_proc=4, split='train')

# Accessing the first row of the dataset
first_row = dataset[0]

# Output: {'subject': '<http://yago-knowledge.org/resource/Sdsscgb_11322_U002E_4_Q85387516>',
#          'predicate': '<http://www.w3.org/2000/01/rdf-schema#comment>',
#          'object': '"galaxy"@en'}

Additional Information

Licensing

The YAGO 4.5 dataset is available under the Creative Commons Attribution-ShareAlike 3.0 license.

Citation

If you use the YAGO 4.5 dataset in your work, please cite the following publication:

@article{suchanek2023integrating,
  title={Integrating the Wikidata Taxonomy into YAGO},
  author={Suchanek, Fabian M and Alam, Mehwish and Bonald, Thomas and Paris, Pierre-Henri and Soria, Jules},
  journal={arXiv preprint arXiv:2308.11884},
  year={2023}
}