child
stringclasses 749
values | parent
stringclasses 900
values | label
int64 0
1
|
---|---|---|
authorize action | allocate action | 1 |
authorize action | accept action | 0 |
authorize action | assign action | 0 |
authorize action | reject action | 0 |
authorize action | pond | 0 |
authorize action | volcano | 0 |
authorize action | preschool | 0 |
authorize action | geospatial geometry | 0 |
authorize action | publication event | 0 |
authorize action | creative work season | 0 |
authorize action | tattoo parlor | 0 |
shopping center | local business | 1 |
shopping center | child care | 0 |
shopping center | real estate agent | 0 |
shopping center | library | 0 |
shopping center | entertainment business | 0 |
shopping center | self storage | 0 |
shopping center | professional service | 0 |
shopping center | internet cafe | 0 |
shopping center | radio station | 0 |
shopping center | food establishment | 0 |
shopping center | recycling center | 0 |
order item | intangible | 1 |
order item | enumeration | 0 |
order item | game server | 0 |
order item | health plan cost sharing specification | 0 |
order item | reservation | 0 |
order item | alignment object | 0 |
order item | observation | 0 |
order item | series | 0 |
order item | demand | 0 |
order item | quantity | 0 |
order item | ticket | 0 |
train station | civic structure | 1 |
train station | educational organization | 0 |
train station | playground | 0 |
train station | zoo | 0 |
train station | museum | 0 |
train station | beach | 0 |
train station | hospital | 0 |
train station | fire station | 0 |
train station | airport | 0 |
train station | public toilet | 0 |
train station | government building | 0 |
aggregate rating | rating | 1 |
aggregate rating | endorsement rating | 0 |
aggregate rating | lodging business | 0 |
aggregate rating | size system enumeration | 0 |
aggregate rating | tv season | 0 |
aggregate rating | day spa | 0 |
aggregate rating | visual artwork | 0 |
aggregate rating | gated residence community | 0 |
aggregate rating | broadcast channel | 0 |
aggregate rating | order | 0 |
aggregate rating | win action | 0 |
statement | creative work | 1 |
statement | quotation | 0 |
statement | short story | 0 |
statement | menu | 0 |
statement | atlas | 0 |
statement | how to step | 0 |
statement | photograph | 0 |
statement | data catalog | 0 |
statement | web page | 0 |
statement | hyper toc entry | 0 |
statement | publication volume | 0 |
d dx element | medical intangible | 1 |
d dx element | medical condition stage | 0 |
d dx element | drug strength | 0 |
d dx element | medical code | 0 |
d dx element | drug legal status | 0 |
d dx element | dose schedule | 0 |
d dx element | geo coordinates | 0 |
d dx element | event series | 0 |
d dx element | update action | 0 |
d dx element | civic structure | 0 |
d dx element | medical business | 0 |
bookmark action | organize action | 1 |
bookmark action | apply action | 0 |
bookmark action | plan action | 0 |
bookmark action | allocate action | 0 |
bookmark action | flight | 0 |
bookmark action | alignment object | 0 |
bookmark action | corporation | 0 |
bookmark action | financial product | 0 |
bookmark action | research organization | 0 |
bookmark action | store | 0 |
bookmark action | bus reservation | 0 |
manuscript | creative work | 1 |
manuscript | quotation | 0 |
manuscript | short story | 0 |
manuscript | menu | 0 |
manuscript | atlas | 0 |
manuscript | how to step | 0 |
manuscript | photograph | 0 |
manuscript | data catalog | 0 |
manuscript | web page | 0 |
manuscript | hyper toc entry | 0 |
manuscript | publication volume | 0 |
comedy club | entertainment business | 1 |
Dataset Card for Schemaorg
This dataset is a collection of Mixed-hop Prediction datasets created from Schema.org's subsumption hierarchy (TBox) for evaluating hierarchy embedding models. It is an evaluation-only dataset consisting of just validation and test splits.
- Mixed-hop Prediction: This task aims to evaluate the model’s capability in determining the existence of subsumption relationships between arbitrary entity pairs, where the entities are not necessarily seen during training. The transfer setting of this task involves training models on asserted training edges of one hierarchy testing on arbitrary entity pairs of another.
See our published paper for more detail.
Links
- GitHub Repository: https://github.com/KRR-Oxford/HierarchyTransformers
- Huggingface Page: https://huggingface.co/Hierarchy-Transformers
- Zenodo Release: https://doi.org/10.5281/zenodo.10511042
- Paper: Language Models as Hierarchy Encoders (NeurIPS 2024).
The information of original entity IDs is not available in the Huggingface release; To map entities back to their original hierarchies, refer to this Zenodo release.
Dataset Structure
Each subset in this dataset follows the naming convention TaskType-NegativeType-SampleStructure
:
TaskType
: EitherMultiHop
orMixedHop
, indicating the type of hierarchy evaluation task.
In this dataset, only
MixedHop
is available.
NegativeType
: EitherRandomNegatives
orHardNegatives
, specifying the strategy used for negative sampling.SampleStructure
: EitherTriplets
orPairs
, indicating the format of the samples.- In
Triplets
, each sample is structured as(child, parent, negative)
. - In
Pairs
, each sample is a labelled pair(child, parent, label)
, wherelabel=1
denotes a positive subsumption andlabel=0
denotes a negative subsumption.
- In
For example, to load a subset for the Mixed-hop Prediction task with random negatives and samples presented as triplets, we can use the following command:
from datasets import load_dataset
dataset = load_dataset("Hierarchy-Transformers/Schemaorg", "MixedHop-RandomNegatives-Triplets")
Dataset Usage
For evaluation, the
Pairs
sample structure should be adopted, as it allows for the computation of Precision, Recall, and F1 scores.For training, the choice between
Pairs
,Triplets
, or more complex sample structures depends on the model's design and specific requirements.
Citation
The relevant paper has been accepted at NeurIPS 2024 (to appear).
@article{he2024language,
title={Language models as hierarchy encoders},
author={He, Yuan and Yuan, Zhangdie and Chen, Jiaoyan and Horrocks, Ian},
journal={arXiv preprint arXiv:2401.11374},
year={2024}
}
Contact
Yuan He (yuan.he(at)cs.ox.ac.uk
)
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