Repairing Ontologies via Axiom Weakening
November 09, 2017 Β· Declared Dead Β· π AAAI Conference on Artificial Intelligence
"No code URL or promise found in abstract"
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Authors
Nicolas Troquard, Roberto Confalonieri, Pietro Galliani, Rafael Penaloza, Daniele Porello, Oliver Kutz
arXiv ID
1711.03430
Category
cs.AI: Artificial Intelligence
Citations
69
Venue
AAAI Conference on Artificial Intelligence
Last Checked
5 months ago
Abstract
Ontology engineering is a hard and error-prone task, in which small changes may lead to errors, or even produce an inconsistent ontology. As ontologies grow in size, the need for automated methods for repairing inconsistencies while preserving as much of the original knowledge as possible increases. Most previous approaches to this task are based on removing a few axioms from the ontology to regain consistency. We propose a new method based on weakening these axioms to make them less restrictive, employing the use of refinement operators. We introduce the theoretical framework for weakening DL ontologies, propose algorithms to repair ontologies based on the framework, and provide an analysis of the computational complexity. Through an empirical analysis made over real-life ontologies, we show that our approach preserves significantly more of the original knowledge of the ontology than removing axioms.
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