Explaining Non-Entailment by Model Transformation for the Description Logic EL
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
Abstract
Reasoning results computed by description logic systems can be hard to comprehend. When an ontology does not entail an expected subsumption relationship, generating an explanation of this non-entailment becomes necessary. In this paper, we use countermodels to explain non-entailments. More precisely, we devise relevant parts of canonical models of EL ontologies that serve as explanations and discuss the computational complexity of extracting these parts by means of model transformations. Furthermore, we provide an implementation of these transformations and evaluate it using real ontologies.
Details
Original language | English |
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Title of host publication | IJCKG '22: Proceedings of the 11th International Joint Conference on Knowledge Graphs |
Editors | Alessandro Artale, Diego Calvanese, Haofen Wang, Xiaowang Zhang |
Pages | 1-9 |
Number of pages | 9 |
ISBN (electronic) | 9781450399876 |
Publication status | Published - 13 Feb 2023 |
Peer-reviewed | Yes |
Publication series
Series | IJCKG: International Joint Conference on Knowledge Graphs |
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External IDs
Scopus | 85148543350 |
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dblp | conf/jist/AlrabbaaH22 |
Mendeley | 8c330099-4ff9-3904-a1a2-76a5bafba995 |
Keywords
ASJC Scopus subject areas
Keywords
- Model Transformation, Explainable AI, Description Logics