Datasets:
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
paperswithcode_id: dbpedia
pretty_name: DBpedia
dataset_info:
config_name: dbpedia_14
features:
- name: label
dtype:
class_label:
names:
'0': Company
'1': EducationalInstitution
'2': Artist
'3': Athlete
'4': OfficeHolder
'5': MeanOfTransportation
'6': Building
'7': NaturalPlace
'8': Village
'9': Animal
'10': Plant
'11': Album
'12': Film
'13': WrittenWork
- name: title
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 178428970
num_examples: 560000
- name: test
num_bytes: 22310285
num_examples: 70000
download_size: 119424374
dataset_size: 200739255
configs:
- config_name: dbpedia_14
data_files:
- split: train
path: dbpedia_14/train-*
- split: test
path: dbpedia_14/test-*
default: true
Dataset Card for DBpedia14
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: More Information Needed
- Repository: https://github.com/zhangxiangxiao/Crepe
- Paper: https://arxiv.org/abs/1509.01626
- Point of Contact: Xiang Zhang
Dataset Summary
The DBpedia ontology classification dataset is constructed by picking 14 non-overlapping classes from DBpedia 2014. They are listed in classes.txt. From each of thse 14 ontology classes, we randomly choose 40,000 training samples and 5,000 testing samples. Therefore, the total size of the training dataset is 560,000 and testing dataset 70,000. There are 3 columns in the dataset (same for train and test splits), corresponding to class index (1 to 14), title and content. The title and content are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). There are no new lines in title or content.
Supported Tasks and Leaderboards
text-classification,topic-classification: The dataset is mainly used for text classification: given the content and the title, predict the correct topic.
Languages
Although DBpedia is a multilingual knowledge base, the DBpedia14 extract contains English data mainly, other languages may appear (e.g. a film whose title is origanlly not English).
Dataset Structure
Data Instances
A typical data point, comprises of a title, a content and the corresponding label.
An example from the DBpedia test set looks as follows:
{
'title':'',
'content':" TY KU /taɪkuː/ is an American alcoholic beverage company that specializes in sake and other spirits. The privately-held company was founded in 2004 and is headquartered in New York City New York. While based in New York TY KU's beverages are made in Japan through a joint venture with two sake breweries. Since 2011 TY KU's growth has extended its products into all 50 states.",
'label':0
}
Data Fields
- 'title': a string containing the title of the document - escaped using double quotes (") and any internal double quote is escaped by 2 double quotes ("").
- 'content': a string containing the body of the document - escaped using double quotes (") and any internal double quote is escaped by 2 double quotes ("").
- 'label': one of the 14 possible topics.
Data Splits
The data is split into a training and test set. For each of the 14 classes we have 40,000 training samples and 5,000 testing samples. Therefore, the total size of the training dataset is 560,000 and testing dataset 70,000.
Dataset Creation
Curation Rationale
The DBPedia ontology classification dataset is constructed by Xiang Zhang ([email protected]), licensed under the terms of the Creative Commons Attribution-ShareAlike License and the GNU Free Documentation License. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).
Source Data
Initial Data Collection and Normalization
Source data is taken from DBpedia: https://wiki.dbpedia.org/develop/datasets
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
The DBPedia ontology classification dataset is constructed by Xiang Zhang ([email protected]), licensed under the terms of the Creative Commons Attribution-ShareAlike License and the GNU Free Documentation License. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).
Licensing Information
The DBPedia ontology classification dataset is licensed under the terms of the Creative Commons Attribution-ShareAlike License and the GNU Free Documentation License.
Citation Information
@inproceedings{NIPS2015_250cf8b5,
author = {Zhang, Xiang and Zhao, Junbo and LeCun, Yann},
booktitle = {Advances in Neural Information Processing Systems},
editor = {C. Cortes and N. Lawrence and D. Lee and M. Sugiyama and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Character-level Convolutional Networks for Text Classification},
url = {https://proceedings.neurips.cc/paper_files/paper/2015/file/250cf8b51c773f3f8dc8b4be867a9a02-Paper.pdf},
volume = {28},
year = {2015}
}
Lehmann, Jens, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N. Mendes, Sebastian Hellmann et al. "DBpedia–a large-scale, multilingual knowledge base extracted from Wikipedia." Semantic web 6, no. 2 (2015): 167-195.
Contributions
Thanks to @hfawaz for adding this dataset.