Abstract
Developing and maintaining ontologies is an expensive and error-prone task. After an error is detected, users may have to wait for a long time before a corrected version of the ontology is available. In the meantime, one might still want to derive meaningful knowledge from the ontology, while avoiding the known errors. We study error-tolerant reasoning tasks in the description logic \(\mathcal{E{\kern-.1em}L}\). While these problems are intractable, we propose methods for improving the reasoning times by precompiling information about the known errors and using proof-theoretic techniques for computing justifications. A prototypical implementation shows that our approach is feasible for large ontologies used in practice.
Partially supported by DFG within the Cluster of Excellence ‘cfAED’.
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Ludwig, M., Peñaloza, R. (2014). Error-Tolerant Reasoning in the Description Logic \(\mathcal{E{\kern-.1em}L}\) . In: Fermé, E., Leite, J. (eds) Logics in Artificial Intelligence. JELIA 2014. Lecture Notes in Computer Science(), vol 8761. Springer, Cham. https://doi.org/10.1007/978-3-319-11558-0_8
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