Measuring Inconsistencies in Ontologies
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Ontologies play a key role in the infrastructure of the Semantic Web
for sharing precisely defined terms which can be made accessible to automated agents. For ontologies with complex knowledge to represent and reason with, errors due to inconsistencies become quite common, and these inconsistencies can be intrinsically different. While there are Description Log.css reasoners that can detect inconsistencies in input ontologies, they do not help classify and/or summarize the nature of the inconsistencies that are present. In this paper, we propose a novel technique based on Shapley values to measure inconsistencies in ontologies. This measure can be used to identify which axioms in an input ontology or which part of these axioms need to be removed or modified in order to make the input consistent. We also propose optimization techniques, such as partitioning, to improve the efficiency of computing Shapley values. The proposed techniques are independent of a particular ontology language and are independent of a particular reasoning system used. Application of this method can improve the quality of ontology diagnosis and repair in general.
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