ABox Abduction for Inconsistent Knowledge Bases under Repair Semantics

TL;DR

This paper explores ABox abduction in inconsistent knowledge bases using repair semantics, analyzing complexity across different variants and description logics.

Contribution

It introduces new notions of abduction for inconsistent KBs and provides a complexity landscape under repair semantics for DL-Lite and EL_bot.

Findings

Complexity results vary across abduction variants and description logics.

Proposes criteria for useful hypotheses in inconsistent KBs.

Extends abduction concepts to repair-based reasoning in inconsistent settings.

Abstract

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.