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        <title>Language Model Analysis for Ontology Subsumption Inference</title>
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    <abstract>Investigating whether pre-trained language models (LMs) can function as knowledge bases (KBs) has raised wide research interests recently. However, existing works focus on simple, triple-based, relational KBs, but omit more sophisticated, logic-based, conceptualised KBs such as OWL ontologies. To investigate an LM’s knowledge of ontologies, we propose OntoLAMA, a set of inference-based probing tasks and datasets from ontology subsumption axioms involving both atomic and complex concepts. We conduct extensive experiments on ontologies of different domains and scales, and our results demonstrate that LMs encode relatively less background knowledge of Subsumption Inference (SI) than traditional Natural Language Inference (NLI) but can improve on SI significantly when a small number of samples are given. We will open-source our code and datasets.</abstract>
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%0 Conference Proceedings
%T Language Model Analysis for Ontology Subsumption Inference
%A He, Yuan
%A Chen, Jiaoyan
%A Jimenez-Ruiz, Ernesto
%A Dong, Hang
%A Horrocks, Ian
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F he-etal-2023-language
%X Investigating whether pre-trained language models (LMs) can function as knowledge bases (KBs) has raised wide research interests recently. However, existing works focus on simple, triple-based, relational KBs, but omit more sophisticated, logic-based, conceptualised KBs such as OWL ontologies. To investigate an LM’s knowledge of ontologies, we propose OntoLAMA, a set of inference-based probing tasks and datasets from ontology subsumption axioms involving both atomic and complex concepts. We conduct extensive experiments on ontologies of different domains and scales, and our results demonstrate that LMs encode relatively less background knowledge of Subsumption Inference (SI) than traditional Natural Language Inference (NLI) but can improve on SI significantly when a small number of samples are given. We will open-source our code and datasets.
%R 10.18653/v1/2023.findings-acl.213
%U https://aclanthology.org/2023.findings-acl.213
%U https://doi.org/10.18653/v1/2023.findings-acl.213
%P 3439-3453

Markdown (Informal)

[Language Model Analysis for Ontology Subsumption Inference](https://aclanthology.org/2023.findings-acl.213) (He et al., Findings 2023)

• Language Model Analysis for Ontology Subsumption Inference (He et al., Findings 2023)

ACL

• Yuan He, Jiaoyan Chen, Ernesto Jimenez-Ruiz, Hang Dong, and Ian Horrocks. 2023. Language Model Analysis for Ontology Subsumption Inference. In Findings of the Association for Computational Linguistics: ACL 2023, pages 3439–3453, Toronto, Canada. Association for Computational Linguistics.