Effective method for detecting error causes from incoherent biological ontologies

Computing the minimal axiom sets (MinAs) for an unsatisfiable class is an important task in incoherent ontology debugging. Ddebugging ontologies based on patterns (DOBP) is a pattern-based debugging method that uses a set of heuristic strategies based on four patterns. Each pattern is represented as a directed graph and the depth-first search strategy is used to find the axiom paths relevant to the MinAs of the unsatisfiable class. However, DOBP is inefficient when a debugging large incoherent ontology with a lot of unsatisfiable classes. To solve the problem, we first extract a module responsible for the erroneous classes and then compute the MinAs based on the extracted module. The basic idea of module extraction is that rather than computing MinAs based on the original ontology O, they are computed based on a module M extracted from O. M provides a smaller search space than O because M is considerably smaller than O. The experimental results on biological ontologies show that the module extracted using the Module-DOBP method is smaller than the original ontology. Lastly, our proposed approach optimized with the module extraction algorithm is more efficient than the DOBP method both for large-scale ontologies and numerous unsatisfiable classes.

Automated summary

This paper introduces a pattern-based ontology debugging method, DOBP, and a more efficient approach of calculating the minimal axiom sets for unsatisfiable classes by extracting modules. The experimental results show that the Module-DOBP method produces smaller modules compared to the original ontology, making the debugging process more efficient for large-scale ontologies with numerous unsatisfiable classes.