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---
name: Schema.org's Subsumption Hierarchy (TBox)
description: >
This dataset is a collection of Mixed-hop Prediction
datasets created from Schema.org's subsumption hierarchy (TBox) for evaluating hierarchy embedding models.
license: apache-2.0
language:
- en
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
task_categories:
- feature-extraction
- sentence-similarity
pretty_name: Schemaorg
tags:
- hierarchy-transformers
- sentence-transformers
configs:
- config_name: MixedHop-RandomNegatives-Triplets
description: >
A dataset for Mixed-hop Prediction with random negatives; samples formatted
as triplets.
data_files:
- split: val
path: MixedHop-RandomNegatives-Triplets/val*
- split: test
path: MixedHop-RandomNegatives-Triplets/test*
- config_name: MixedHop-HardNegatives-Triplets
description: >
A dataset for Mixed-hop Prediction with hard negatives; samples formatted as
triplets.
data_files:
- split: val
path: MixedHop-HardNegatives-Triplets/val*
- split: test
path: MixedHop-HardNegatives-Triplets/test*
- config_name: MixedHop-RandomNegatives-Pairs
description: >
A dataset for Mixed-hop Prediction with random negatives; samples formatted
as pairs.
data_files:
- split: val
path: MixedHop-RandomNegatives-Pairs/val*
- split: test
path: MixedHop-RandomNegatives-Pairs/test*
- config_name: MixedHop-HardNegatives-Pairs
description: >
A dataset for Mixed-hop Prediction with hard negatives; samples formatted as
pairs.
data_files:
- split: val
path: MixedHop-HardNegatives-Pairs/val*
- split: test
path: MixedHop-HardNegatives-Pairs/test*
---
# 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.
<!-- - **Multi-hop Inference**: This task aims to evaluate the model’s ability in deducing indirect, multi-hop subsumptions from direct, one-hop subsumptions, so as to simulate transitive inference. -->
- **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](https://arxiv.org/abs/2401.11374) 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](https://arxiv.org/abs/2401.11374) (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](https://doi.org/10.5281/zenodo.10511042).
## Dataset Structure
Each subset in this dataset follows the naming convention `TaskType-NegativeType-SampleStructure`:
- `TaskType`: Either `MultiHop` or `MixedHop`, indicating the type of hierarchy evaluation task.
> In this dataset, only `MixedHop` is available.
- `NegativeType`: Either `RandomNegatives` or `HardNegatives`, specifying the strategy used for negative sampling.
- `SampleStructure`: Either `Triplets` or `Pairs`, 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)`, where `label=1` denotes a positive subsumption and `label=0` denotes a negative subsumption.
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:
```python
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
*Yuan He, Zhangdie Yuan, Jiaoyan Chen, Ian Horrocks.* **Language Models as Hierarchy Encoders.** Advances in Neural Information Processing Systems 37 (NeurIPS 2024).
```
@inproceedings{NEURIPS2024_1a970a3e,
author = {He, Yuan and Yuan, Moy and Chen, Jiaoyan and Horrocks, Ian},
booktitle = {Advances in Neural Information Processing Systems},
editor = {A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang},
pages = {14690--14711},
publisher = {Curran Associates, Inc.},
title = {Language Models as Hierarchy Encoders},
url = {https://proceedings.neurips.cc/paper_files/paper/2024/file/1a970a3e62ac31c76ec3cea3a9f68fdf-Paper-Conference.pdf},
volume = {37},
year = {2024}
}
```
## Contact
Yuan He (`yuan.he(at)cs.ox.ac.uk`) |