Datasets:

krr-oxford
/

OntoLAMA

	---
	license: apache-2.0
	task_categories:
	- text-classification
	tags:
	- Ontologies
	- Subsumption Inference
	- Natural Language Inference
	- Conceptual Knowledge
	- LMs-as-KBs
	pretty_name: OntoLAMA
	size_categories:
	- 1M<n<10M
	language:
	- en
	dataset_info:
	- config_name: schemaorg-atomic-SI
	features:
	- name: v_sub_concept
	dtype: string
	- name: v_super_concept
	dtype: string
	- name: label
	dtype:
	class_label:
	names:
	'0': negative_subsumption
	'1': positive_subsumption
	- name: axiom
	dtype: string
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	- config_name: doid-atomic-SI
	features:
	- name: v_sub_concept
	dtype: string
	- name: v_super_concept
	dtype: string
	- name: label
	dtype:
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	'1': positive_subsumption
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	dtype: string
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	- config_name: foodon-atomic-SI
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	- name: v_sub_concept
	dtype: string
	- name: v_super_concept
	dtype: string
	- name: label
	dtype:
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	'0': negative_subsumption
	'1': positive_subsumption
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	- config_name: go-atomic-SI
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	- name: v_sub_concept
	dtype: string
	- name: v_super_concept
	dtype: string
	- name: label
	dtype:
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	'0': negative_subsumption
	'1': positive_subsumption
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	dtype: string
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	- config_name: bimnli
	features:
	- name: premise
	dtype: string
	- name: hypothesis
	dtype: string
	- name: label
	dtype:
	class_label:
	names:
	'0': contradiction
	'1': entailment
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	- config_name: foodon-complex-SI
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	- name: v_sub_concept
	dtype: string
	- name: v_super_concept
	dtype: string
	- name: label
	dtype:
	class_label:
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	'0': negative_subsumption
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	dtype: string
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	- name: v_sub_concept
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	- name: v_super_concept
	dtype: string
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	---

	# OntoLAMA: LAnguage Model Analysis for Ontology Subsumption Inference


	### Dataset Summary

	OntoLAMA is a set of language model (LM) probing datasets for ontology subsumption inference.
	The work follows the "LMs-as-KBs" literature 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
	Natural Language Inference (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 sampled axioms are verified through ontology reasoning. The SI task is further divided into Atomic SI and
	Complex SI where the former involves only atomic named concepts and the latter involves both atomic and complex concepts.
	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.

	See dataset specifications on [DeepOnto](https://krr-oxford.github.io/DeepOnto/ontolama/).

	See the published paper on [Arxiv](https://arxiv.org/abs/2302.06761) or [ACL Anthology](https://aclanthology.org/2023.findings-acl.213/).

	### 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


	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': 'ham and cheese sandwich that derives from some lima bean (whole)',
	'v_super_concept': 'lima bean substance',
	'label': 0,
	'axiom': 'SubClassOf(ObjectIntersectionOf(<http://purl.obolibrary.org/obo/FOODON_03307824> ObjectSomeValuesFrom(<http://purl.obolibrary.org/obo/RO_0001000> <http://purl.obolibrary.org/obo/FOODON_03302053>)) <http://purl.obolibrary.org/obo/FOODON_00002776>)',
	'anchor_axiom': 'EquivalentClasses(<http://purl.obolibrary.org/obo/FOODON_00002776> ObjectIntersectionOf(<http://purl.obolibrary.org/obo/FOODON_00002000> ObjectSomeValuesFrom(<http://purl.obolibrary.org/obo/RO_0001000> <http://purl.obolibrary.org/obo/FOODON_03302053>)) )'
	}
	```


	An example in the biMNLI dataset created from the MNLI dataset is as follows:
	```
	{
	'premise': 'At the turn of the 19th century Los Angeles and Salt Lake City were among the burgeoning metropolises of the new American West.',
	'hypothesis': 'Salt Lake City was booming in the early 19th century.',
	'label': 1
	}
	```

	### Data Fields

	#### SI 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`.

	#### biMNLI Data Fields
	- `premise`: inheritated from the MNLI dataset.
	- `hypothesis`: inheritated from the MNLI dataset.
	- `label`: a binary class label indicating `contradiction` (`0`) or `entailment` (`1`).

	### Data Splits

	\| Source \| #NamedConcepts \| #EquivAxioms \| #Dataset (Train/Dev/Test) \|
	\|------------\|----------------\|--------------\|------------------------------------------------------------------------\|
	\| Schema.org \| 894 \| - \| Atomic SI: 808/404/2,830 \|
	\| DOID \| 11,157 \| - \| Atomic SI: 90,500/11,312/11,314 \|
	\| FoodOn \| 30,995 \| 2,383 \| Atomic SI: 768,486/96,060/96,062 <br /> Complex SI: 3,754/1,850/13,080 \|
	\| GO \| 43,303 \| 11,456 \| Atomic SI: 772,870/96,608/96,610 <br /> Complex SI: 72,318/9,040/9,040 \|
	\| MNLI \| - \| - \| biMNLI: 235,622/26,180/12,906 \|


	### Licensing Information

	Creative Commons Attribution 4.0 International

	### Citation Information

	The relevant paper has been accepted at Findings of ACL 2023.

	```
	@inproceedings{he-etal-2023-language,
	title = "Language Model Analysis for Ontology Subsumption Inference",
	author = "He, Yuan and
	Chen, Jiaoyan and
	Jimenez-Ruiz, Ernesto and
	Dong, Hang and
	Horrocks, Ian",
	booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
	month = jul,
	year = "2023",
	address = "Toronto, Canada",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/2023.findings-acl.213",
	doi = "10.18653/v1/2023.findings-acl.213",
	pages = "3439--3453"
	}
	```