Modern ontology debugging methods allow efficient identification and localization of faulty axioms in an ontology. However, in many use cases such as ontology alignment the ontologies might include many conflict sets, i.e. sets of axioms preserving the faults, thus making ontology diagnosis infeasible. In this paper we present a debugging approach based on a direct computation of diagnoses that omits calculation of conflict sets. The evaluation results show that the approach is practicable and is able to identify a fault in adequate time.
the algorithm attempts to reconstruct the tree by reusing the remaining valid diagnoses. In the direct approach limiting the number of diagnoses used to compute a query to some reasonable number. e.g. n = 10 results in a small size of the search tree, thus, using less memory in comparison to the standard approach.
We evaluated the direct ontology debugging technique using aligned ontologies
generated in the framework of OAEI 2011 [
Moreover, in the first experiment we evaluated the efficiency of the interactive direct
debugging approach applied to the 13 “hard” ontologies. We selected the target
diagnosis randomly among all diagnoses that included only invalid alignments suggested by a
system. The latter can be computed using the set of correct alignments provided by the
organizers of OAEI 2011. In the experiment the used the Entropy scoring function [
In the second scenario we applied the direct method to unsatisfiable and classifiable within 2 hours ontologies, generated for the Anatomy problem. The source ontologies O1 and O2 include 11545 and 4838 axioms correspondingly, whereas the size of the alignments varies between 1147 and 1461 axioms. The target diagnosis selection process was performed in the same way as in the first experiment. The results of the experiment show that the target diagnosis can be computed within 40 second in an average case. Moreover, INV-HS-TREE slightly outperformed HS-TREE.