Language Model Analysis for Ontology Subsumption Inference

• URL: http://arxiv.org/abs/2302.06761v3
• Date: Mon, 8 May 2023 17:21:40 GMT
• Title: Language Model Analysis for Ontology Subsumption Inference
• Authors: Yuan He, Jiaoyan Chen, Ernesto Jim\'enez-Ruiz, Hang Dong, Ian Horrocks
• Abstract summary: We investigate whether pre-trained language models (LMs) can function as knowledge bases (KBs) We propose OntoLAMA, a set of inference-based probing tasks and datasets from subsumption axioms involving both atomic and complex concepts. We conduct extensive experiments on 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) We will open-source our code and datasets.
• Score: 37.00562636991463
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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