Language Model Analysis for Ontology Subsumption Inference
Yuan He, Jiaoyan Chen, Ernesto Jimenez-Ruiz, Hang Dong, Ian Horrocks
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.- Anthology ID:
- 2023.findings-acl.213
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3439–3453
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.213
- DOI:
- 10.18653/v1/2023.findings-acl.213
- Cite (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.
- Cite (Informal):
- Language Model Analysis for Ontology Subsumption Inference (He et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2023.findings-acl.213.pdf