It is now widely accepted that in order to optimize both their usage and their design and maintenance ontologies should comply to design quality criteria, e.g., absence of redundancies and appropriate level of abstraction. Yet given the variety and scope of activities comprised in the lifecycle of an ontological model (OM), such as adapting, splitting, populating, this quality is easily compromised, especially with ontologies of larger size and/or resulting from the merge of smaller ones. Conversely, restoring it through refactoring, i.e., restructuring of the ontology to improve defects, is knowingly a challenging task as relocating an ontology element can adversely affect its neighbors. We investigate here a holistic refactoring approach that, given an ontology, amounts to presenting its designer with a list of the most plausible abstract entities missing in it. The core of the approach is a recently devised concept analysis method, called relational, that allows deeper refactoring by feeding into the process various ontological relations, e.g., concept-to-property incidences. The focus here is put on the NLP-aspects of the refactoring, while we also provide some preliminary results from a series of validating experiments.

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