---
license: apache-2.0
task_categories:
- text-classification
tags:
- Ontologies
- Subsumption Inference
- Natural Language Inference
pretty_name: OntoLAMA
size_categories:
- 1M<n<10M
language:
- en
---

# OntoLAMA: LAnguage Model Analysis for Ontology Subsumption Inference

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-instances)
  - [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)

## Dataset Description

- **Homepage:** https://krr-oxford.github.io/DeepOnto/ontolama
- **Repository:** https://github.com/KRR-Oxford/DeepOnto
- **Paper:** https://arxiv.org/abs/2302.06761
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]

### Dataset Summary

OntoLAMA is a set of language model (LM) probing datasets for ontology subsumption inference. The work follows the "LMs-as-KBs" literatue but focuses on conceptualised knowledge extracted from formalised KBs such as the OWL ontologies. Specifically, the subsumption inference (SI) task is introduced and formulated in the NLI style, where the sub-concept and the super-concept involved in a subsumption axiom are verbalised and fitted into a template to form the premise and hypothesis, respectively. The SI task is futher divided into Atomic SI and Complex SI where the former involves only atomic named concepts and the latter involves complex concept expressions restricted to OWL 2 EL. Real-world ontologies of different scales and domains are used for constructing OntoLAMA and in total there are four Atomic SI datasets and two Complex SI datasets.



### Supported Tasks and Leaderboards

[Needs More Information]

### Languages

The text in the dataset is in English, as used in the source ontologies. The associated BCP-47 code is `en`.

## Dataset Structure

### Data Instances

A typical SI data point comprises a verbalised sub-concept `v_sub_concept`, a verbalised super-concept 'v_super_concept', a binary label indicating whether these two concepts have a subsumption relationship or not (with `1` referring to a positive subsumption), and a string representation of the original subsumption axiom before verbalisation. 

An example in the Atomic SI dataset created from the Gene Ontology (GO) is as follows:
```
{
    'v_sub_concept': 'ctpase activity',
    'v_super_concept': 'ribonucleoside triphosphate phosphatase activity',
    'label': 1,
    'axiom': 'SubClassOf(<http://purl.obolibrary.org/obo/GO_0043273> <http://purl.obolibrary.org/obo/GO_0017111>)'
}
```

An example in the Complex SI dataset created from the Food Ontology (FoodOn) is as follows:
```
{
    'v_sub_concept': '...',
    'v_super_concept': '...',
    'label': 0,
    'axiom': ...,
    'anchor_axiom': ...,
}
```

### Data Fields

- `v_sub_concept`: verbalised sub-concept expression.
- `v_super_concept`: verbalised super-concept expression.
- `label`: a binary class label indicating whether two concepts really form a subsumption relationship (`1` means yes).
- `axiom`: a string representation of the original subsumption axiom which is useful for tracing back to the ontology.
- `anchor_axiom`: (for complex SI only) a string representation of the anchor equivalence axiom used for sampling the `axiom`.

### Data Splits

[Needs More Information]

## Dataset Creation

### Curation Rationale

[Needs More Information]

### Source Data

#### Initial Data Collection and Normalization

[Needs More Information]

#### Who are the source language producers?

[Needs More Information]

### Annotations

#### Annotation process

[Needs More Information]

#### Who are the annotators?

[Needs More Information]

### Personal and Sensitive Information

[Needs More Information]

## Considerations for Using the Data

### Social Impact of Dataset

[Needs More Information]

### Discussion of Biases

[Needs More Information]

### Other Known Limitations

[Needs More Information]

## Additional Information

### Dataset Curators

[Needs More Information]

### Licensing Information

[Needs More Information]

### Citation Information

[Needs More Information]