ABox Abduction for Inconsistent Knowledge Bases under Repair Semantics
It extends ABox abduction to handle inconsistent data, which is important for applications like diagnosis and repair, but the contribution is incremental as it adapts existing concepts to a new setting.
The paper defines ABox abduction for inconsistent knowledge bases under repair semantics, providing complexity results for DL-Lite and EL_bot logics.
Given a knowledge base (KB) with a non-entailed fact, the ABox abduction problem asks for possible extensions of the KB that would entail this fact. This problem has many applications, ranging from diagnosis to explainability and repair. ABox abduction has been well-investigated for consistent KBs and classical semantics, but little is known for the case of inconsistent KBs, which can be caused by erroneous data. In this paper we define suitable notions of abduction in this setting and propose criteria that guide abduction towards "useful" hypotheses. To regain meaningful reasoning in the presence of inconsistencies, we use well-established repair semantics. We provide a comprehensive landscape of the complexity of ABox abduction under repair semantics, treating different variants of the abduction problem for the light-weight description logics DL-Lite and EL_bot.