Abstract
Several efforts that leverage the tools of formal ontology have demonstrated the fruitfulness of considering key metaproperties of classes in ontology engineering. Despite that, it is still a common practice to apply representation schemes and approaches-such as OWL-that do not benefit from identifying ontological categories and simply treat all classes in the same manner. In the original study, we proposed an approach to support the automated classification of classes into the ontological categories underlying the (g)UFO foundational ontology. We proposed a set of inference rules derived from (g)UFO's axiomatization that, given an initial classification of the classes in an OWL ontology, supports the inference of the classification for the remaining classes in the ontology. We formalized these rules, implemented them in a tool, and assessed them against a catalog of ontologies.
Original language | English |
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Title of host publication | Proceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024 |
Editors | Kate Larson |
Publisher | International Joint Conferences on Artificial Intelligence |
Pages | 8373-8377 |
Number of pages | 5 |
ISBN (Electronic) | 9781956792041 |
State | Published - 2024 |
Externally published | Yes |
Event | 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024 - Jeju, Korea, Republic of Duration: 3 Aug 2024 → 9 Aug 2024 |
Publication series
Name | IJCAI International Joint Conference on Artificial Intelligence |
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ISSN (Print) | 1045-0823 |
Conference
Conference | 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024 |
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Country/Territory | Korea, Republic of |
City | Jeju |
Period | 3/08/24 → 9/08/24 |
Bibliographical note
Publisher Copyright:© 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.
ASJC Scopus subject areas
- Artificial Intelligence