README.md

metadata

name: SnomedCT's Subsumption Hierarchy (TBox)
description: >
  This dataset is a collection of Multi-hop Inference and Mixed-hop Prediction
  datasets created from SnomedCT's subsumption hierarchy (TBox) for training and
  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: SnomedCT
tags:
  - hierarchy-transformers
  - sentence-transformers
configs:
  - config_name: MultiHop-RandomNegatives-Triplets
    description: >
      A dataset for Multi-hop Inference with random negatives; samples formatted
      as triplets.
    data_files:
      - split: train
        path: MultiHop-RandomNegatives-Triplets/train*
      - split: val
        path: MultiHop-RandomNegatives-Triplets/val*
      - split: test
        path: MultiHop-RandomNegatives-Triplets/test*
  - config_name: MultiHop-HardNegatives-Triplets
    description: >
      A dataset for Multi-hop Inference with hard negatives; samples formatted
      as triplets.
    data_files:
      - split: train
        path: MultiHop-HardNegatives-Triplets/train*
      - split: val
        path: MultiHop-HardNegatives-Triplets/val*
      - split: test
        path: MultiHop-HardNegatives-Triplets/test*
  - config_name: MixedHop-RandomNegatives-Triplets
    description: >
      A dataset for Mixed-hop Prediction with random negatives; samples
      formatted as triplets.
    data_files:
      - split: train
        path: MixedHop-RandomNegatives-Triplets/train*
      - 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: train
        path: MixedHop-HardNegatives-Triplets/train*
      - split: val
        path: MixedHop-HardNegatives-Triplets/val*
      - split: test
        path: MixedHop-HardNegatives-Triplets/test*
  - config_name: MultiHop-RandomNegatives-Pairs
    description: >
      A dataset for Multi-hop Inference with random negatives; samples formatted
      as pairs.
    data_files:
      - split: train
        path: MultiHop-RandomNegatives-Pairs/train*
      - split: val
        path: MultiHop-RandomNegatives-Pairs/val*
      - split: test
        path: MultiHop-RandomNegatives-Pairs/test*
  - config_name: MultiHop-HardNegatives-Pairs
    description: >
      A dataset for Multi-hop Inference with hard negatives; samples formatted
      as pairs.
    data_files:
      - split: train
        path: MultiHop-HardNegatives-Pairs/train*
      - split: val
        path: MultiHop-HardNegatives-Pairs/val*
      - split: test
        path: MultiHop-HardNegatives-Pairs/test*
  - config_name: MixedHop-RandomNegatives-Pairs
    description: >
      A dataset for Mixed-hop Prediction with random negatives; samples
      formatted as pairs.
    data_files:
      - split: train
        path: MixedHop-RandomNegatives-Pairs/train*
      - 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: train
        path: MixedHop-HardNegatives-Pairs/train*
      - split: val
        path: MixedHop-HardNegatives-Pairs/val*
      - split: test
        path: MixedHop-HardNegatives-Pairs/test*

Dataset Card for SnomedCT

This dataset is a collection of Multi-hop Inference and Mixed-hop Prediction datasets created from SnomedCT's subsumption hierarchy (TBox) for training and evaluating hierarchy embedding models.

• 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 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: Either MultiHop or MixedHop, indicating the type of hierarchy evaluation task.

• 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:

from datasets import load_dataset
dataset = load_dataset("Hierarchy-Transformers/SnomedCT", "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)