Schockaert, Steven  ORCID: https://orcid.org/0000-0002-9256-2881, Ibanez Garcia, Yazmin ORCID: https://orcid.org/0000-0002-1276-904X and Gutierrez Basulto, Victor ORCID: https://orcid.org/0000-0002-6117-5459
2021.
A description logic for analogical reasoning.
Presented at: 30th International Joint Conference on Artificial Intelligence (IJCAI 2021),
Virtual,
21-26 August 2021.
Published in: Zhou, Zhi-Hua ed.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence.
International Joint Conferences on Artificial Intelligence Organization,
pp. 2040-2046.
10.24963/ijcai.2021/281
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Abstract
Ontologies formalise how the concepts from a given domain are interrelated. Despite their clear potential as a backbone for explainable AI, existing ontologies tend to be highly incomplete, which acts as a significant barrier to their more widespread adoption. To mitigate this issue, we present a mechanism to infer plausible missing knowledge, which relies on reasoning by analogy. To the best of our knowledge, this is the first paper that studies analogical reasoning within the setting of description logic ontologies. After showing that the standard formalisation of analogical proportion has important limitations in this setting, we introduce an alternative semantics based on bijective mappings between sets of features. We then analyse the properties of analogies under the proposed semantics, and show among others how it enables two plausible inference patterns: rule translation and rule extrapolation.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Publisher: | International Joint Conferences on Artificial Intelligence Organization |
| Date of First Compliant Deposit: | 4 June 2021 |
| Date of Acceptance: | 29 April 2021 |
| Last Modified: | 10 Jul 2025 14:15 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/141726 |
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